Daily Papers

We introduce Nemotron-Labs-Diffusion, a tri-mode language model (LM) that unifies AR, diffusion, and self-speculation decoding within a single architecture. Trained with a joint AR-diffusion objective, Nemotron-Labs-Diffusion can switch modes to sustain high throughput across deployment settings and concurrency levels. Our study shows that (1) AR and diffusion objectives are complementary: diffusion improves lookahead planning, while AR provides left-to-right linguistic priors. (2) In self-speculation mode, diffusion drafts while AR verifies, outperforming multi-token prediction (MTP) methods in both acceptance rate and real-device efficiency. (3) A speed-of-light analysis further demonstrates diffusion's long-term potential, with up to 76.5% more tokens per forward pass than self-speculation under an optimal sampler. Scaling to 3B, 8B, and 14B parameters, our Nemotron-Labs-Diffusion family, including base, instruct, and vision-language models, consistently outperforms state-of-the-art open-source AR and diffusion LMs in both accuracy and speed. For example, Nemotron-Labs-Diffusion-8B decodes 6x more tokens per forward than Qwen3-8B with comparable accuracy, translating to 4x higher throughput on SPEED-Bench with SGLang on a GB200 GPU.

How fast could a deep-learning model run on target hardware, and how far is today's implementation from that limit? These questions are central to software, hardware, and algorithm optimizations. Speed-of-Light (SOL) analysis answers them by computing a workload's theoretical minimum execution time on a given architecture. Yet deriving SOL bounds remains manual, error-prone, and disconnected from rapid model development. To close this gap, we introduce SOLAR, a framework that automatically derives validated SOL bounds from PyTorch and JAX source code. SOLAR leverages both generative and deterministic components in its flow: an LLM frontend translates any source programs into an executable Affine Loop IR, validated by output comparison; a deterministic flow lifts the IR into an einsum graph; and an analytical backend computes unfused, fused, and cache-aware SOL bounds. SOLAR provides comprehensive operator and language coverage, produces validated bounds with zero observed SOL violations, and offers multi-fidelity analysis that tightens bounds and surfaces optimization insights. We evaluate SOLAR across KernelBench, JAX/Flax models, and robotics workloads. These experiments demonstrate four use cases: headroom analysis at multiple fidelity levels, identifying optimization opportunities, cross-platform exploration, and inverse-roofline hardware provisioning.

As agentic AI systems become increasingly capable of generating and optimizing GPU kernels, progress is constrained by benchmarks that reward speedup over software baselines rather than proximity to hardware-efficient execution. We present SOL-ExecBench, a benchmark of 235 CUDA kernel optimization problems extracted from 124 production and emerging AI models spanning language, diffusion, vision, audio, video, and hybrid architectures, targeting NVIDIA Blackwell GPUs. The benchmark covers forward and backward workloads across BF16, FP8, and NVFP4, including kernels whose best performance is expected to rely on Blackwell-specific capabilities. Unlike prior benchmarks that evaluate kernels primarily relative to software implementations, SOL-ExecBench measures performance against analytically derived Speed-of-Light (SOL) bounds computed by SOLAR, our pipeline for deriving hardware-grounded SOL bounds, yielding a fixed target for hardware-efficient optimization. We report a SOL Score that quantifies how much of the gap between a release-defined scoring baseline and the hardware SOL bound a candidate kernel closes. To support robust evaluation of agentic optimizers, we additionally provide a sandboxed harness with GPU clock locking, L2 cache clearing, isolated subprocess execution, and static analysis based checks against common reward-hacking strategies. SOL-ExecBench reframes GPU kernel benchmarking from beating a mutable software baseline to closing the remaining gap to hardware Speed-of-Light.

We allow the Higgs field Phi to interact with a dilaton field chi of the background spacetime via the coupling chi^2,Phi^daggerPhi. Upon spontaneous gauge symmetry breaking, the Higgs VEV becomes proportional to chi. While traditionally this linkage is employed to make the Planck mass and particle masses dependent on chi, we present an textit alternative mechanism: the Higgs VEV will be used to construct Planck's constant hbar and speed of light c. Specifically, each open set vicinity of a given point x^* on the spacetime manifold is equipped with a replica of the Glashow-Weinberg-Salam action operating with its own effective values of hbar_* and c_* per hbar_*proptochi^{-1/2}(x^*) and c_*proptochi^{1/2}(x^*), causing these ``fundamental constants'' to vary alongside the dynamical field chi. Moreover, in each open set around x^*, the prevailing value chi(x^*) determines the length and time scales for physical processes occurring in this region as lproptochi^{-1}(x^*) and tauproptochi^{-3/2}(x^*). This leads to an textit anisotropic relation tau^{-1}propto l^{-3/2} between the rate of clocks and the length of rods, resulting in a distinct set of novel physical phenomena. For late-time cosmology, the variation of c along the trajectory of light waves from distant supernovae towards the Earth-based observer necessitates modifications to the Lema\^itre redshift relation and the Hubble law. These modifications are capable of: (1) Accounting for the Pantheon Catalog of SNeIa through a declining speed of light in an expanding Einstein--de Sitter universe, thus avoiding the need for dark energy; (2) Revitalizing Blanchard-Douspis-Rowan-Robinson-Sarkar's CMB power spectrum analysis that bypassed dark energy [A&A 412, 35 (2003)]; and (3) Resolving the H_0 tension without requiring a dynamical dark energy component.

Forthcoming space-based gravitational-wave (GW) detectors will employ second-generation time-delay interferometry (TDI) to suppress laser frequency noise and achieve the sensitivity required for GW detection. We introduce an inverse light-path operator P_{i_{1}i_{2}i_{3}ldots i_{n-1}i_{n}}, which enables simple representation of second-generation TDI combinations and a concise description of light propagation. Analytical expressions and high-accuracy approximate formulas are derived for the sky- and polarization-averaged response functions, noise power spectral densities (PSDs), and sensitivity curves of TDI Michelson, (alpha,beta,gamma), Monitor, Beacon, Relay, and Sagnac combinations, as well as their orthogonal A, E, T channels. Our results show that: (i) second-generation TDIs have the same sensitivities as their first-generation counterparts; (ii) the A, E, T sensitivities and the optimal sensitivity are independent of the TDI generation and specific combination; (iii) the A and E channels have equal averaged responses, noise PSDs, and sensitivities, while the T channel has much weaker response and sensitivity at low frequencies (2pi fL/clesssim3); (iv) except for the (alpha,beta,gamma) and zeta combinations and the T channel, all sensitivity curves exhibit a flat section in the range f_{n}<flesssim 1.5/(2pi L/c), where the noise-balance frequency f_{n} separates the proof-mass- and optical-path-dominated regimes, while the response-transition frequency sim 1.5/(2pi L/c) separates the response function's low- and high-frequency behaviors; (v) the averaged response, noise PSD, and sensitivity of zeta scales with those of the T channel. These analytical and approximate formulations provide useful benchmarks for instrument optimization and data-analysis studies for future space-based GW detectors.

Scene reconstruction in the presence of high-speed motion and low illumination is important in many applications such as augmented and virtual reality, drone navigation, and autonomous robotics. Traditional motion estimation techniques fail in such conditions, suffering from too much blur in the presence of high-speed motion and strong noise in low-light conditions. Single-photon cameras have recently emerged as a promising technology capable of capturing hundreds of thousands of photon frames per second thanks to their high speed and extreme sensitivity. Unfortunately, traditional computer vision techniques are not well suited for dealing with the binary-valued photon data captured by these cameras because these are corrupted by extreme Poisson noise. Here we present a method capable of estimating extreme scene motion under challenging conditions, such as low light or high dynamic range, from a sequence of high-speed image frames such as those captured by a single-photon camera. Our method relies on iteratively improving a motion estimate by grouping and aggregating frames after-the-fact, in a stratified manner. We demonstrate the creation of high-quality panoramas under fast motion and extremely low light, and super-resolution results using a custom single-photon camera prototype. For code and supplemental material see our https://wisionlab.com/project/panoramas-from-photons/{project webpage}.

Femtosecond thermal lens spectroscopy (FTLS) is a powerful analytical tool, yet its application to complex, multi-component mixtures like fragrance accords remains limited. Here, we introduce and validate a unified metric, the Femtosecond Thermal Lens Integrated Magnitude (FTL-IM), to characterize such mixtures. The FTL-IM, derived from the integrated signal area, provides a direct, model-free measure of the total thermo-optical response, including critical convective effects. Applying the FTL-IM to complex six-component accords, we demonstrate its utility in predicting a mixture's thermal response from its composition through linear additivity with respect to component mole fractions. Our method quantifies the accords' behavior, revealing both the baseline contributions of components and the dominant, non-linear effects of highly-active species like Methyl Anthranilate. This consistency is validated across single-beam Z-scan, dual-beam Z-scan, and time-resolved FTLS measurements. The metric also demonstrates the necessity of single-beam measurements for interpreting dual-beam data. This work establishes a rapid, quantitative method for fragrance analysis, offering advantages for quality control by directly linking a mixture's bulk thermo-optical properties to its composition.

We study the problem of estimating the time delay between two signals representing delayed, irregularly sampled and noisy versions of the same underlying pattern. We propose and demonstrate an evolutionary algorithm for the (hyper)parameter estimation of a kernel-based technique in the context of an astronomical problem, namely estimating the time delay between two gravitationally lensed signals from a distant quasar. Mixed types (integer and real) are used to represent variables within the evolutionary algorithm. We test the algorithm on several artificial data sets, and also on real astronomical observations of quasar Q0957+561. By carrying out a statistical analysis of the results we present a detailed comparison of our method with the most popular methods for time delay estimation in astrophysics. Our method yields more accurate and more stable time delay estimates: for Q0957+561, we obtain 419.6 days for the time delay between images A and B. Our methodology can be readily applied to current state-of-the-art optical monitoring data in astronomy, but can also be applied in other disciplines involving similar time series data.

In this work, we study the black hole light echoes in terms of the two-photon autocorrelation and explore their connection with the quasinormal modes. It is shown that the above time-domain phenomenon can be analyzed by utilizing the well-known frequency-domain relations between the quasinormal modes and characteristic parameters of null geodesics. We found that the time-domain correlator, obtained by the inverse Fourier transform, naturally acquires the echo feature, which can be attributed to a collective effect of the asymptotic poles through a weighted summation of the squared modulus of the relevant Green's functions. Specifically, the contour integral leads to a summation taking over both the overtone index and angular momentum. Moreover, the dominant contributions to the light echoes are from those in the eikonal limit, consistent with the existing findings using the geometric-optics arguments. For the Schwarzschild black holes, we demonstrate the results numerically by considering a transient spherical light source. Also, for the Kerr spacetimes, we point out a potential difference between the resulting light echoes using the geometric-optics approach and those obtained by the black hole perturbation theory. Possible astrophysical implications of the present study are addressed.

Since their discovery, AGN light curves are known to be intrinsically variable. In the optical/UV band, this variability is consistent with correlated or red noise and is particularly well described by the damped random walk (DRW) model. In this work, we evaluate the feasibility of a new method for identifying spatially unresolved couples of AGN through a fully Bayesian time-domain analysis of the observed light curves (LCs). More specifically, we check whether observed LCs are better described by a single DRW, which we interpret as emitted by a single massive black hole (MBH), or a pair of independent DRWs, generated by a pair of MBHs. We test the method on mock LCs associated with a single MBH and pairs generated with different cadences and lengths of observational campaigns. We constrained the occurrence of false positives, that is, the percentage of single MBH LCs that show substantial evidence in favour of the unresolved MBH pair scenario, finding a fraction of 0.2% and 0.59% in the even and uneven sampling scenarios. We discuss how well the method recovers the model parameters, showing that about 51% and 7% of the simulated LCs have all the recovered parameters within 20% of their true values in our best scenario of evenly sampled LCs for the single MBH and MBH pair scenarios, respectively. We finally study the region of the parameter space in which the detection of an MBH pair is possible, finding that such objects can be correctly identified if the timescales of the process describing the noise are very different, with a ratio smaller than ~0.2, and the variability amplitudes are similar, with their ratio bigger than ~0.2. When limiting to such a region of the parameter space, the fraction of pairs with all the recovered parameters within 20% of the injected values increases up to about 14% and 8% for evenly and unevenly sampled LCs, respectively.

With its exquisite sensitivity, wavelength coverage, and spatial and spectral resolution, the James Webb Space Telescope is poised to revolutionise our view of the distant, high-redshift (z>5) Universe. While Webb's spectroscopic observations will be transformative for the field, photometric observations play a key role in identifying distant objects and providing more comprehensive samples than accessible to spectroscopy alone. In addition to identifying objects, photometric observations can also be used to infer physical properties and thus be used to constrain galaxy formation models. However, inferred physical properties from broadband photometric observations, particularly in the absence of spectroscopic redshifts, often have large uncertainties. With the development of new tools for forward modelling simulations it is now routinely possible to predict observational quantities, enabling a direct comparison with observations. With this in mind, in this work, we make predictions for the colour evolution of galaxies at z=5-15 using the FLARES: First Light And Reionisation Epoch Simulations cosmological hydrodynamical simulation suite. We predict a complex evolution, driven predominantly by strong nebular line emission passing through individual bands. These predictions are in good agreement with existing constraints from Hubble and Spitzer as well as some of the first results from Webb. We also contrast our predictions with other models in the literature: while the general trends are similar we find key differences, particularly in the strength of features associated with strong nebular line emission. This suggests photometric observations alone should provide useful discriminating power between different models.

Recent space-borne and ground-based observations provide photometric measurements as time series. The effect of interstellar dust extinction in the near-infrared range is only 10% of that measured in the V band. However, the sensitivity of the light curve shape to the physical parameters in the near-infrared is much lower. So, interpreting these types of data sets requires new approaches like the different large-scale surveys, which create similar problems with big data. Using a selected data set, we provide a method for applying routines implemented in R to extract most information of measurements to determine physical parameters, which can also be used in automatic classification schemes and pipeline processing. We made a multivariate classification of 131 Cepheid light curves (LC) in J, H, and K colors, where all the LCs were represented in 20D parameter space in these colors separately. Performing a Principal Component Analysis (PCA), we got an orthogonal coordinate system and squared Euclidean distances between LCs, with 6 significant eigenvalues, reducing the 20-dimension to 6. We also estimated the optimal number of partitions of similar objects and found it to be equal to 7 in each color; their dependence on the period, absolute magnitude, amplitude, and metallicity are also discussed. We computed the Spearman rank correlations, showing that periods and absolute magnitudes correlate with the first three PCs significantly. The first two PC are also found to have a relationship with the amplitude, but the metallicity effects are only marginal. The method shown can be generalized and implemented in unsupervised classification schemes and analysis of mixed and biased samples. The analysis of our Classical Cepheid near-infrared LC sample showed that the J, H, K curves are insufficient for determination of stellar metallicity, with mass being the key factor shaping them.

We introduce a novel method for measuring properties of periodic phenomena with an event camera, a device asynchronously reporting brightness changes at independently operating pixels. The approach assumes that for fast periodic phenomena, in any spatial window where it occurs, a very similar set of events is generated at the time difference corresponding to the frequency of the motion. To estimate the frequency, we compute correlations of spatio-temporal windows in the event space. The period is calculated from the time differences between the peaks of the correlation responses. The method is contactless, eliminating the need for markers, and does not need distinguishable landmarks. We evaluate the proposed method on three instances of periodic phenomena: (i) light flashes, (ii) vibration, and (iii) rotational speed. In all experiments, our method achieves a relative error lower than 0.04%, which is within the error margin of ground truth measurements.

Here we present 1701 light curves of 1550 spectroscopically confirmed Type Ia supernovae (SNe Ia) that will be used to infer cosmological parameters as part of the Pantheon+ SN analysis and the SH0ES (Supernovae and H0 for the Equation of State of dark energy) distance-ladder analysis. This effort is one part of a series of works that perform an extensive review of redshifts, peculiar velocities, photometric calibration, and intrinsic-scatter models of SNe Ia. The total number of light curves, which are compiled across 18 different surveys, is a significant increase from the first Pantheon analysis (1048 SNe), particularly at low redshift (z). Furthermore, unlike in the Pantheon analysis, we include light curves for SNe with z<0.01 such that SN systematic covariance can be included in a joint measurement of the Hubble constant (H_0) and the dark energy equation-of-state parameter (w). We use the large sample to compare properties of 151 SNe Ia observed by multiple surveys and 12 pairs/triplets of "SN siblings" - SNe found in the same host galaxy. Distance measurements, application of bias corrections, and inference of cosmological parameters are discussed in the companion paper by Brout et al. (2022b), and the determination of H_0 is discussed by Riess et al. (2022). These analyses will measure w with sim3% precision and H_0 with 1 km/s/Mpc precision.

We present an imaging and neural rendering technique that seeks to synthesize videos of light propagating through a scene from novel, moving camera viewpoints. Our approach relies on a new ultrafast imaging setup to capture a first-of-its kind, multi-viewpoint video dataset with picosecond-level temporal resolution. Combined with this dataset, we introduce an efficient neural volume rendering framework based on the transient field. This field is defined as a mapping from a 3D point and 2D direction to a high-dimensional, discrete-time signal that represents time-varying radiance at ultrafast timescales. Rendering with transient fields naturally accounts for effects due to the finite speed of light, including viewpoint-dependent appearance changes caused by light propagation delays to the camera. We render a range of complex effects, including scattering, specular reflection, refraction, and diffraction. Additionally, we demonstrate removing viewpoint-dependent propagation delays using a time warping procedure, rendering of relativistic effects, and video synthesis of direct and global components of light transport.

Modern-day time-domain photometric surveys collect a lot of observations of various astronomical objects and the coming era of large-scale surveys will provide even more information on their properties. Spectroscopic follow-ups are especially crucial for transients such as supernovae and most of these objects have not been subject to such studies. }{Flux time series are actively used as an affordable alternative for photometric classification and characterization, for instance, peak identifications and luminosity decline estimations. However, the collected time series are multidimensional and irregularly sampled, while also containing outliers and without any well-defined systematic uncertainties. This paper presents a search for the best-performing methods to approximate the observed light curves over time and wavelength for the purpose of generating time series with regular time steps in each passband.}{We examined several light curve approximation methods based on neural networks such as multilayer perceptrons, Bayesian neural networks, and normalizing flows to approximate observations of a single light curve. Test datasets include simulated PLAsTiCC and real Zwicky Transient Facility Bright Transient Survey light curves of transients.}{The tests demonstrate that even just a few observations are enough to fit the networks and improve the quality of approximation, compared to state-of-the-art models. The methods described in this work have a low computational complexity and are significantly faster than Gaussian processes. Additionally, we analyzed the performance of the approximation techniques from the perspective of further peak identification and transients classification. The study results have been released in an open and user-friendly Fulu Python library available on GitHub for the scientific community.

Aims: The aim of the present study is to explore how to disentangle energy-dependent time delays due to a possible Lorentz invariance violation (LIV) at Planck scale from intrinsic delays expected in standard blazar flares. Methods: We first characterise intrinsic time delays in BL Lacs and Flat Spectrum Radio Quasars in standard one-zone time-dependent synchrotron self-Compton or external Compton models, during flares produced by particle acceleration and cooling processes. We simulate families of flares with both intrinsic and external LIV-induced energy-dependent delays. Discrimination between intrinsic and LIV delays is then investigated in two different ways. A technique based on Euclidean distance calculation between delays obtained in the synchrotron and in the inverse-Compton spectral bumps is used to assess their degree of correlation. A complementary study is performed using spectral hardness versus intensity diagrams in both energy ranges. Results: We show that the presence of non-negligible LIV effects, which essentially act only at very high energies (VHE), can drastically reduce the strong correlation expected between the X-ray and the VHE gamma-ray emission in leptonic scenarios. The LIV phenomenon can then be hinted at measuring the Euclidean distance d_{E} from simultaneous X-ray and gamma-ray flare monitoring. Large values of minimal distance d_{E,min} would directly indicate the influence of non-intrinsic time delays possibly due to LIV in SSC flares. LIV effects can also significantly modify the VHE hysteresis patterns in hardness-intensity diagrams and even change their direction of rotation as compared to the X-ray behaviour. Both observables could be used to discriminate between LIV and intrinsic delays, provided high quality flare observations are available.

We present cosmological analysis of 12 nearby (z<0.06) Type IIP supernovae (SNe IIP) observed with the ROTSE-IIIb telescope. To achieve precise photometry, we present a new image differencing technique that is implemented for the first time on the ROTSE SN photometry pipeline. With this method, we find up to a 20\% increase in the detection efficiency and significant reduction in residual RMS scatter of the SN lightcurves when compared to the previous pipeline performance. We use the published optical spectra and broadband photometry of well studied SNe IIP to establish temporal models for ejecta velocity and photospheric temperature evolution for our SNe IIP population. This study yields measurements that are competitive to other methods even when the data are limited to a single epoch during the photospheric phase of SNe IIP. Using the fully reduced ROTSE photometry and optical spectra, we apply these models to the respective photometric epochs for each SN in the ROTSE IIP sample. This facilitates the use of the Expanding Photosphere Method (EPM) to obtain distance estimates to their respective host galaxies. We then perform cosmological parameter fitting using these EPM distances from which we measure the Hubble constant to be 72.9^{+5.7}_{-4.3}~{rm kms^{-1}~Mpc^{-1}}, which is consistent with the standard Lambda CDM model values derived using other independent techniques.

We present new time delays, the main ingredient of time delay cosmography, for 22 lensed quasars resulting from high-cadence r-band monitoring on the 2.6 m ESO VLT Survey Telescope and Max-Planck-Gesellschaft 2.2 m telescope. Each lensed quasar was typically monitored for one to four seasons, often shared between the two telescopes to mitigate the interruptions forced by the COVID-19 pandemic. The sample of targets consists of 19 quadruply and 3 doubly imaged quasars, which received a total of 1 918 hours of on-sky time split into 21 581 wide-field frames, each 320 seconds long. In a given field, the 5-{\sigma} depth of the combined exposures typically reaches the 27th magnitude, while that of single visits is 24.5 mag - similar to the expected depth of the upcoming Vera-Rubin LSST. The fluxes of the different lensed images of the targets were reliably de-blended, providing not only light curves with photometric precision down to the photon noise limit, but also high-resolution models of the targets whose features and astrometry were systematically confirmed in Hubble Space Telescope imaging. This was made possible thanks to a new photometric pipeline, lightcurver, and the forward modelling method STARRED. Finally, the time delays between pairs of curves and their uncertainties were estimated, taking into account the degeneracy due to microlensing, and for the first time the full covariance matrices of the delay pairs are provided. Of note, this survey, with 13 square degrees, has applications beyond that of time delays, such as the study of the structure function of the multiple high-redshift quasars present in the footprint at a new high in terms of both depth and frequency. The reduced images will be available through the European Southern Observatory Science Portal.

We extend our previous work on applying CMB techniques to the mapping of gravitational-wave backgrounds to backgrounds which have non-GR polarisations. Our analysis and results are presented in the context of pulsar-timing array observations, but the overarching methods are general, and can be easily applied to LIGO or eLISA observations using appropriately modified response functions. Analytic expressions for the pulsar-timing response to gravitational waves with non-GR polarisation are given for each mode of a spin-weighted spherical-harmonic decomposition of the background, which permit the signal to be mapped across the sky to any desired resolution. We also derive the pulsar-timing overlap reduction functions for the various non-GR polarisations, finding analytic forms for anisotropic backgrounds with scalar-transverse ("breathing") and vector-longitudinal polarisations, and a semi-analytic form for scalar-longitudinal backgrounds. Our results indicate that pulsar-timing observations will be completely insensitive to scalar-transverse mode anisotropies in the polarisation amplitude beyond dipole, and anisotropies in the power beyond quadrupole. Analogously to our previous findings that pulsar-timing observations lack sensitivity to tensor-curl modes for a transverse-traceless tensor background, we also find insensitivity to vector-curl modes for a vector-longitudinal background.

We performed variability analysis of the multiwavelength light curves for the flat-spectrum radio quasar PKS 0727-11. Using the generalized Lomb-Scargle periodogram, we identified a possible quasi-periodic oscillation (QPO) of sim 168.6 days (persisted for 6 cycles, with a significance of 3.8sigma) in the gamma-ray light curve during the flare period (MJD 54687-55738). It is the first time that periodic variations have been detected in this source, and further supported by other methods: weighted wavelet z-transform, phase dispersion minimization, REDFIT, autoregressive integrated moving average model, and structure function analysis. Cross-correlation analysis shows that there is a strong correlation between multi-band light variations, indicating that gamma-ray and radio flares may originate from the same disturbance, and the distance between the emission regions of gamma-ray and radio flares is calculated based on the time lag. We demonstrate that QPO arising from the non-ballistic helical jet motion driven by the orbital motion in a supermassive binary black hole is a plausible physical explanation. In this scenario, the estimated mass of the primary black hole is Msim3.66times10^8-5.79times10^{9}M_odot.

The out-of-band energy loss caused by in-built color filters significantly degrades the signal-to-noise ratio and the dynamic range of conventional image sensors, which has restricted the attempt to develop ultrahigh-density imaging devices by merely shrinking the pixel size. This issue will be more serious for security cameras which need to collect visible (VIS) light and near-infrared (NIR) photons as well. The existing solutions mostly explore complex photonic nanostructures, which are often too complicated for production. In this work, we demonstrate a pixel-scale spectral router utilizing two-dimensional (2D) Si3N4 Mie scattering metagratings that can spatially divide NIR (850 nm) and VIS (400-700 nm) light to different pixels at high efficiencies. It has a minimum feature size larger than 360 nm, highly promising for massive production. Compared with the traditional filter design, our router can gain about 42% and 30% signal enhancement for NIR and VIS band, respectively. We show that it also has good polarization insensitivity and incident angle tolerance. The NIR-VIS simultaneous imaging is inspected without any complex reconstruction algorithm. Mode analysis indicates that the multipolar scattering of our Mie-type metagratings provides the necessary degrees of freedom to spatially optimize the routing functions for broadband photons.

Time series data and their time-frequency representation from gravitational-wave interferometers present multiple opportunities for the use of artificial intelligence methods associated with signal and image processing. Closely connected with this is the real-time aspect associated with gravitational-wave interferometers and the astrophysical observations they perform; the discovery potential of these instruments can be significantly enhanced when data processing can be achieved in O(1s) timescales. In this work, we introduce a novel signal and noise identification tool based on the YOLO (You Only Look Once) object detection framework. For its application into gravitational waves, we will refer to it as GW-YOLO. This tool can provide scene identification capabilities and essential information regarding whether an observed transient is any combination of noise and signal. Additionally, it supplies detailed time-frequency coordinates of the detected objects in the form of pixel masks, an essential property that can be used to understand and characterize astrophysical sources, as well as instrumental noise. The simultaneous identification of noise and signal, combined with precise pixel-level localization, represents a significant advancement in gravitational-wave data analysis. Our approach yields a 50\% detection efficiency for binary black hole signals at a signal-to-noise ratio (SNR) of 15 when such signals overlap with transient noise artifacts. When noise artifacts overlap with binary neutron star signals, our algorithm attains 50\% detection efficiency at an SNR of 30. This presents the first quantitative assessment of the ability to detect astrophysical events overlapping with realistic, instrument noise present in gravitational-wave interferometers.

Materials combining both a high refractive index and a wide band gap are of great interest for optoelectronic and sensor applications. However, these two properties are typically described by an inverse correlation with high refractive index appearing in small gap materials and vice-versa. Here, we conduct a first-principles high-throughput study on more than 4000 semiconductors (with a special focus on oxides). Our data confirm the general inverse trend between refractive index and band gap but interesting outliers are also identified. The data are then analyzed through a simple model involving two main descriptors: the average optical gap and the effective frequency. The former can be determined directly from the electronic structure of the compounds, but the latter cannot. This calls for further analysis in order to obtain a predictive model. Nonetheless, it turns out that the negative effect of a large band gap on the refractive index can counterbalanced in two ways: (i) by limiting the difference between the direct band gap and the average optical gap which can be realized by a narrow distribution in energy of the optical transitions and (ii) by increasing the effective frequency which can be achieved through either a high number of transitions from the top of the valence band to the bottom of the conduction or a high average probability for these transitions. Focusing on oxides, we use our data to investigate how the chemistry influences this inverse relationship and rationalize why certain classes of materials would perform better. Our findings can be used to search for new compounds in many optical applications both in the linear and non-linear regime (waveguides, optical modulators, laser, frequency converter, etc.).

The instantaneous emission from a relativistic surface endowed with a Lorentz factor Gamma that decreases away from the outflow symmetry axis can naturally explain the three phases observed by Swift/XRT in GRBs and their afterglows (GRB tail, afterglow plateau and post-plateau). We expand the analytical formalism of the "Larger-Angle Emission" model previously developed for "Power-Law" outflows to "n-Exponential" outflows (e.g. exponential with n=1 and Gaussian with n=2) and compare their abilities to account for the X-ray emission of XRT afterglows. We assume power-law Gamma-dependences of two spectral characteristics (peak-energy and peak intensity) and find that, unlike Power-Law outflows, n-Exponential outflows cannot account for plateaus with a temporal dynamical range larger than 100. To include all information existing in the Swift/XRT measurements of X-ray aferglows (0.3-10 keV unabsorbed flux and effective spectral slope), we calculate 0.3 keV and 10 keV light-curves using a broken power-law emission spectrum of peak-energy and low-and high-energy slopes that are derived from the effective slope measured by XRT. This economical peak-energy determination is found to be consistent with more expensive spectral fits. The angular distributions of the Lorentz factor, comoving frame peak-energy, and peak-intensity (Gamma (theta), E'_p (theta), i'_p(theta)) constrain the (yet-to-be determined) convolution of various features of the production of relativistic jets by solar-mass black-holes and of their propagation through the progenitor/circumburst medium, while the E'_p (Gamma) and i'_p (Gamma) dependences may constrain the GRB dissipation mechanism and the GRB emission process.

Time-domain surveys such as the Zwicky Transient Facility (ZTF) have opened a new frontier in the discovery and characterization of transients. While photometric light curves provide broad temporal coverage, spectroscopic observations remain crucial for physical interpretation and source classification. However, existing spectral analysis methods -- often reliant on template fitting or parametric models -- are limited in their ability to capture the complex and evolving spectra characteristic of such sources, which are sometimes only available at low resolution. In this work, we introduce SpectraNet, a deep convolutional neural network designed to learn robust representations of optical spectra from transients. Our model combines multi-scale convolution kernels and multi-scale pooling to extract features from preprocessed spectra in a hierarchical and interpretable manner. We train and validate SpectraNet on low-resolution time-series spectra obtained from the Spectral Energy Distribution Machine (SEDM) and other instruments, demonstrating state-of-the-art performance in classification. Furthermore, in redshift prediction tasks, SpectraNet achieves a root mean squared relative redshift error of 0.02, highlighting its effectiveness in precise regression tasks as well.

We present observations and timing analyses of 68 millisecond pulsars (MSPs) comprising the 15-year data set of the North American Nanohertz Observatory for Gravitational Waves (NANOGrav). NANOGrav is a pulsar timing array (PTA) experiment that is sensitive to low-frequency gravitational waves. This is NANOGrav's fifth public data release, including both "narrowband" and "wideband" time-of-arrival (TOA) measurements and corresponding pulsar timing models. We have added 21 MSPs and extended our timing baselines by three years, now spanning nearly 16 years for some of our sources. The data were collected using the Arecibo Observatory, the Green Bank Telescope, and the Very Large Array between frequencies of 327 MHz and 3 GHz, with most sources observed approximately monthly. A number of notable methodological and procedural changes were made compared to our previous data sets. These improve the overall quality of the TOA data set and are part of the transition to new pulsar timing and PTA analysis software packages. For the first time, our data products are accompanied by a full suite of software to reproduce data reduction, analysis, and results. Our timing models include a variety of newly detected astrometric and binary pulsar parameters, including several significant improvements to pulsar mass constraints. We find that the time series of 23 pulsars contain detectable levels of red noise, 10 of which are new measurements. In this data set, we find evidence for a stochastic gravitational-wave background.

In metrology, Fisher information is an important metric that quantifies the precision that can be achieved in a measurement. For optical measurements using coherent light it has been shown that Fisher information can be expressed simply using the scattering matrix of the system. Fisher information can be maximized over the input modes to achieve maximum information states, which produce optimally precise estimates for a parameter when the system is limited by photon noise. Here, we extend this approach to multiparameter estimation, in which case Fisher information takes the form of a matrix. We consider several scalar functions of the Fisher matrix to optimize the precision in multiple parameters at the same time. We also consider strategies for dealing with nuisance parameters, which can degrade the achievable precision of other parameters but are not of interest to measure. We corroborate our findings numerically using a scattering system of 2D coupled dipoles.

The Kramers-Kronig relations are a pivotal foundation of linear optics and atomic physics, embedding a physical connection between the real and imaginary components of any causal response function. A mathematically equivalent, but simpler, approach instead utilises the Hilbert transform. In a previous study, the Hilbert transform was applied to absorption spectra in order to infer the sole refractive index of an atomic medium in the absence of an external magnetic field. The presence of a magnetic field causes the medium to become birefringent and dichroic, and therefore it is instead characterised by two refractive indices. In this study, we apply the same Hilbert transform technique to independently measure both refractive indices of a birefringent atomic medium, leading to an indirect measurement of atomic magneto-optical rotation. Key to this measurement is the insight that inputting specific light polarisations into an atomic medium induces absorption associated with only one of the refractive indices. We show this is true in two configurations, commonly referred to in literature as the Faraday and Voigt geometries, which differ by the magnetic field orientation with respect to the light wavevector. For both cases, we measure the two refractive indices independently for a Rb thermal vapour in a 0.6 T magnetic field, finding excellent agreement with theory. This study further emphasises the application of the Hilbert transform to the field of quantum and atomic optics in the linear regime.

The planned space-based gravitational wave detector, LISA, will provide a fundamentally new means of studying the orbital alignment of close white dwarf binaries. However, due to the inherent symmetry of their gravitational wave signals, a fourfold degeneracy arises in the transverse projections of their angular momentum vectors. In this paper, we demonstrate that by incorporating timing information from electromagnetic observations, such as radial velocity modulations and light curves, this degeneracy can be reduced to twofold.

The exact location of the gamma-ray emitting region in blazar jets has long been a matter of debate. However, the location has important implications about the emission processes, geometric and physical parameters of the jet, as well as the nature of interaction of the jet with the interstellar and intergalactic medium. Diverse conclusions have been drawn by various authors based on a variety of methods applied to different data sets of many blazars, e.g., the location is less than 0.1 pc from the central engine within the broad line region (BLR) or a few or tens of pc downstream beyond the dusty torus or at some intermediate distance. Here we use a method, established in a previous work, in which the location of the GeV/optical emission is determined using the ratio of energy dissipated during contemporaneous outbursts at those wave bands. We apply it to a total of 47 multi-wavelength outbursts in 10 blazars. We find that the location of the GeV/optical emission is beyond the BLR for all cases. This result is consistent with other studies, in which the location has been determined for a large sample of blazars. We compare the location determined by our method for several GeV outbursts of multiple blazars to that obtained by other authors using different methods. We find that our results are consistent in such one-to-one comparison in most cases, for which the required data were available.

We develop a novel way to probe subgalactic-scale matter distribution with diffractive lensing on gravitational waves. Five-year observations from Einstein Telescope and DECIGO are expected to probe k= 10^5sim 10^8 ,{rm Mpc}^{-1} down to P(k) = 10^{-16} sim 10^{-14} ,{rm Mpc}^3 level. These results can be interpreted in terms of primordial black holes in the range M_{rm PBH} gtrsim 10^{-3}M_odot down to f_{rm PBH} = 10^{-6} level, or QCD axion minihalos in the range m_a = 10^{-3} sim 10^{-12} ,{rm eV}. A key result of the paper is the approximate relation between the scale k and the gravitational wave frequency f, derived in an ensemble of `multi-lensing' events. This relation enables direct measurement of the power spectrum at specific scales, with sensitivities characterized by model-independent kernels delta P(k). Additionally, we delineate the statistical properties of `multi-lensing' based on the `Fresnel number' N_F. When N_F cal O(1), the statistical significance can be approximately calculated by Variance of lensing effects, which is directly related to the power spectrum among other moments of matter distribution.

While the temporal variations of the spectro-polarimetric nature of pulsars remains unexplored, this investigation offers significant potential for uncovering key insights into pulsar emission mechanisms, magnetic field geometry, and propagation effects within the magnetosphere. We developed a package for investigating time-varying spectral behavior for full Stokes parameters and demonstrate it on a millisecond pulsar (MSP) J2144-5237 in a binary system (orbital period ~10 days) using the Parkes UWL receiver. In this study we report rotation measure (RM) variation with orbital phase. We find that the temporal variations in the spectra of Stokes I, Q, and V are generally correlated throughout the orbit, while Stokes U exhibits intervals of both correlation and anticorrelation with Stokes I, depending on the orbital phase. We also provide a Poincare sphere representation of the polarization properties of J2144-5237, demonstrating a systematic temporal change of Poincare sphere location for the main component with orbital phase. To our knowledge, this is the first investigation of time-varying properties of the spectro-polarimetric nature of any pulsars or MSPs. Extending this study to probe the spectro-temporal nature of full Stokes data on a larger sample of MSPs or pulsars has the potential to provide vital information on emission mechanisms inside the magnetosphere, interstellar propagation effects, and binary interactions.

Strong gravitational lensing of variable sources, such as quasars or supernovae, can be used to constrain cosmological parameters through a technique known as "time-delay cosmography''. Competitive constraints on the Hubble constant have been achieved with electromagnetic observations of lensed quasars and lensed supernovae. Gravitational wave (GW) astronomy may open up a new channel for time-delay cosmography with GW signal replacing the electromagnetic (EM) one. We highlight the similarities of using GW signals to be applied to time-delay cosmography compared to EM signal. We then discuss key differences between GW and EM signals and their resulting advantages and inconveniences from the angle of the current state-of-the-art using quasars and lensed supernovae for time-delay cosmography. We identify the astrometric precision requirement of the images as a key challenge to overcome and highlight the potentially significant impact that near-perfect time-delay measurements of lensed GWs can bring to the table.

The PRObabilistic Value-Added Bright Galaxy Survey (PROVABGS) catalog will provide measurements of galaxy properties, such as stellar mass (M_*), star formation rate ({rm SFR}), stellar metallicity (Z_{rm MW}), and stellar age (t_{rm age, MW}), for >10 million galaxies of the DESI Bright Galaxy Survey. Full posterior distributions of the galaxy properties will be inferred using state-of-the-art Bayesian spectral energy distribution (SED) modeling of DESI spectroscopy and Legacy Surveys photometry. In this work, we present the SED model, Bayesian inference framework, and methodology of PROVABGS. Furthermore, we apply the PROVABGS SED modeling on realistic synthetic DESI spectra and photometry, constructed using the L-GALAXIES semi-analytic model. We compare the inferred galaxy properties to the true galaxy properties of the simulation using a hierarchical Bayesian framework to quantify accuracy and precision. Overall, we accurately infer the true M_*, {rm SFR}, Z_{rm MW}, and t_{rm age, MW} of the simulated galaxies. However, the priors on galaxy properties induced by the SED model have a significant impact on the posteriors. They impose a {rm SFR}{>}10^{-1} M_odot/{rm yr} lower bound on {rm SFR}, a {sim}0.3 dex bias on log Z_{rm MW} for galaxies with low spectral signal-to-noise, and t_{rm age, MW} < 8,{rm Gyr} upper bound on stellar age. This work also demonstrates that a joint analysis of spectra and photometry significantly improves the constraints on galaxy properties over photometry alone and is necessary to mitigate the impact of the priors. With the methodology presented and validated in this work, PROVABGS will maximize information extracted from DESI observations and provide a probabilistic value-added galaxy catalog that will extend current galaxy studies to new regimes and unlock cutting-edge probabilistic analyses.

The prompt emission of gamma-ray bursts (GRBs) is supposed to be released from the relativistic jet launched from the central engine. Apart from the non-thermal nature of the spectra in a majority of GRBs, there is evidence for the presence of quasi-thermal components in the prompt emission of a few GRBs according to observations by Fermi satellite. On the other hand, the GRB jet has been revealed as structured in recent research. The theoretical observed spectra of photosphere emissions by an off-axis observer and the dependence of the spectra on the viewing angle under the assumption of structured jets remain unexplored. In this paper, we numerically calculate the instantaneous photosphere spectra by different viewing angles from a structured jet, from which relevant temporal and spectral characteristics are derived. Moreover, we address the necessity of proper treatment of the outflow boundary in the photosphere emission scenario. Furthermore, our calculations suggest that the Einstein Probe and Space-based multi-band astronomical Variable Object Monitor will have the capability to detect the short GRBs similar to GRB 170817A up to a luminosity distance of 200Mpc if the off-axis viewing angle is less than 10 degrees.

The characterization and analysis of light curves are vital for understanding the physical and rotational properties of artificial space objects such as satellites, rocket stages, and space debris. This paper introduces the Light Curve Dataset Creator (LCDC), a Python-based toolkit designed to facilitate the preprocessing, analysis, and machine learning applications of light curve data. LCDC enables seamless integration with publicly available datasets, such as the newly introduced Mini Mega Tortora (MMT) database. Moreover, it offers data filtering, transformation, as well as feature extraction tooling. To demonstrate the toolkit's capabilities, we created the first standardized dataset for rocket body classification, RoBo6, which was used to train and evaluate several benchmark machine learning models, addressing the lack of reproducibility and comparability in recent studies. Furthermore, the toolkit enables advanced scientific analyses, such as surface characterization of the Atlas 2AS Centaur and the rotational dynamics of the Delta 4 rocket body, by streamlining data preprocessing, feature extraction, and visualization. These use cases highlight LCDC's potential to advance space debris characterization and promote sustainable space exploration. Additionally, they highlight the toolkit's ability to enable AI-focused research within the space debris community.

We report on analysis of the two-color VR CCD observations of the newly discovered variable 2MASS J18024395+4003309=VSX J180243.9+400331 obtained using the 1-m telescope of the Mt. Lemmon Observatory (LOAO) in the field of the intermediate polar V1323 Her. The extended version of this conference talk we published in 2015JASS...32..127A. The variability was reported in 2012OAP....25..150A, and the object was monitored. The two-color observations covered all phase interval. The object is classified as an Algol-type variable with tidally distorted components, and shows an asymmetry of the maxima (the O\'Connell effect). For phenomenological modeling, we used the trigonometric polynomial approximation of statistically optimal degree, and a recent method "NAV" (New Algol Variable) using local specific shapes for the eclipse. Methodological aspects are described, especially for the case of few color observations. Estimates of the physical parameters based on analysis of phenomenological parameters, are presented.

A low frequency stochastic background of gravitational waves may be detected by pulsar timing experiments in the next five to ten years. Using methods developed to analyze interferometric gravitational wave data, in this paper we lay out the optimal techniques to detect a background of gravitational waves using a pulsar timing array. We show that for pulsar distances and gravitational wave frequencies typical of pulsar timing experiments, neglecting the effect of the metric perturbation at the pulsar does not result in a significant deviation from optimality. We discuss methods for setting upper limits using the optimal statistic, show how to construct skymaps using the pulsar timing array, and consider several issues associated with realistic analysis of pulsar timing data.

Strongly lensed variable quasars can serve as precise cosmological probes, provided that time delays between the image fluxes can be accurately measured. A number of methods have been proposed to address this problem. In this paper, we explore in detail a new approach based on kernel regression estimates, which is able to estimate a single time delay given several datasets for the same quasar. We develop realistic artificial data sets in order to carry out controlled experiments to test of performance of this new approach. We also test our method on real data from strongly lensed quasar Q0957+561 and compare our estimates against existing results.

Superconducting nanowire single-photon detectors are widely used in various fields of physics and technology, due to their high efficiency and timing precision. Although, in principle, their detection mechanism offers broadband operation, their wavelength range has to be optimized by the optical cavity parameters for a specific task. We present a study of the optical absorption of a superconducting nanowire single photon detector (SNSPD) with an optical cavity. The optical properties of the niobium nitride films, measured by spectroscopic ellipsometry, were modelled using the Drude-Lorentz model with quantum corrections. The numerical simulations of the optical response of the detectors show that the wavelength range of the detector is not solely determined by its geometry, but the optical conductivity of the disordered thin metallic films contributes considerably. This contribution can be conveniently expressed by the ratio of imaginary and real parts of the optical conductivity. This knowledge can be utilized in detector design.

With the development of aerospace technology, the increasing population of space debris has posed a great threat to the safety of spacecraft. However, the low intensity of reflected light and high angular velocity of space debris impede the extraction. Besides, due to the limitations of the ground observation methods, small space debris can hardly be detected, making it necessary to enhance the spacecraft's capacity for space situational awareness (SSA). Considering that traditional methods have some defects in low-SNR target detection, such as low effectiveness and large time consumption, this paper proposes a method for low-SNR streak extraction based on local contrast and maximum likelihood estimation (MLE), which can detect space objects with SNR 2.0 efficiently. In the proposed algorithm, local contrast will be applied for crude classifications, which will return connected components as preliminary results, and then MLE will be performed to reconstruct the connected components of targets via orientated growth, further improving the precision. The algorithm has been verified with both simulated streaks and real star tracker images, and the average centroid error of the proposed algorithm is close to the state-of-the-art method like ODCC. At the same time, the algorithm in this paper has significant advantages in efficiency compared with ODCC. In conclusion, the algorithm in this paper is of high speed and precision, which guarantees its promising applications in the extraction of high dynamic targets.

We provide a theoretical analysis for a recently demonstrated cooling method. Two-level particles undergo successive adiabatic transfers upon interaction with counter-propagating laser beams that are repeatedly swept over the transition frequency. We show that particles with narrow linewidth transitions can be cooled to near the recoil limit. This cooling mechanism has a reduced reliance on spontaneous emission compared to Doppler cooling, and hence shows promise for application to systems lacking closed cycling transitions, such as molecules.

Frequency response function (FRF) measurements are widely used in Gravitational Wave (GW) detectors, e.g., for the design of controllers, calibrating signals and diagnostic problems with system dynamics. The aim of this paper is to present GraFIT: a toolbox that enables fast, inexpensive, and accurate identification of FRF measurements for GW detectors compared to the commonly used approaches, including common spectral analysis techniques. The toolbox consists of a single function to estimate the frequency response function for both open-loop and closed-loop systems and for arbitrary input and output dimensions. The toolbox is validated on two experimental case studies of the Virgo detector, illustrating more than a factor 3 reduction in standard deviation of the estimate for the same measurement times, and comparable standard deviations with up to 10 times less data for the new method with respect to the currently implemented Spectral Analysis method.

Fast moving celestial objects are characterized by velocities across the celestial sphere that significantly differ from the motions of background stars. In observational images, these objects exhibit distinct shapes, contrasting with the typical appearances of stars. Depending on the observational method employed, these celestial entities may be designated as near-Earth objects or asteroids. Historically, fast moving celestial objects have been observed using ground-based telescopes, where the relative stability of stars and Earth facilitated effective image differencing techniques alongside traditional fast moving celestial object detection and classification algorithms. However, the growing prevalence of space-based telescopes, along with their diverse observational modes, produces images with different properties, rendering conventional methods less effective. This paper presents a novel algorithm for detecting fast moving celestial objects within star fields. Our approach enhances state-of-the-art fast moving celestial object detection neural networks by transforming them into physical-inspired neural networks. These neural networks leverage the point spread function of the telescope and the specific observational mode as prior information; they can directly identify moving fast moving celestial objects within star fields without requiring additional training, thereby addressing the limitations of traditional techniques. Additionally, all neural networks are integrated using the mixture of experts technique, forming a comprehensive fast moving celestial object detection algorithm. We have evaluated our algorithm using simulated observational data that mimics various observations carried out by space based telescope scenarios and real observation images. Results demonstrate that our method effectively detects fast moving celestial objects across different observational modes.

Fast radio bursts (FRBs) are increasingly being used for cosmological applications such as measuring the Hubble constant and baryon abundance. The increasing number of localized FRBs and precise measurement of dispersion measure (DM) make them a suitable probe for such an approach. We use a sample of 110 localized FRBs as well as a small sub-sample of 24 FRBs with scattering timescale measurements or limits. We infer the Hubble constant (H_0) and the DM distribution of the host galaxies simultaneously by fitting our model to the FRB DM measurements. With current data, our results are in agreement with both high and low redshift measurements of H_0, obtained using Cosmic Microwave Background (CMB) and Type Ia supernovae data respectively. We project that with about 200 localized FRBs, we would be in a position to distinguish between the two scenarios at 4sigma confidence. In addition, the host DM is expected to be related to star formation in the host galaxy and the stellar age of the progenitors. We show that young progenitors with an age of less than 1 Myr are consistent with our inferred distribution of host DM at 95 percent confidence. These young sources may be associated with long scatter broadening times and large DM from their source environments. Indeed, we find that scatter broadening times of FRBs are inconsistent with the Milky Way ISM, but at the same time, do not appear to be strongly correlated with the FRBs' redshift or with the SFR or stellar mass of their host galaxies. This suggests that scattering is dominated by the immediate environment of the sources.

The Stokes V parameter characterizes asymmetry of amplitudes between right- and left-handed waves, and non-vanishing value of the V parameter yields a circularly polarized signal. Cosmologically, V parameter may be a direct probe for parity violation in the universe. In this paper, we theoretically investigate a measurement of this parameter, particularly focusing on the gravitational-wave backgrounds observed via ground-based interferometers. In contrast to the traditional analysis that only considers the total amplitude (or equivalently Omega_{GW}), the signal analysis including a circular-polarized mode has a rich structure due to the multi-dimensionality of target parameters. We show that, by using the network of next-generation detectors, separation between polarized and unpolarized modes can be performed with small statistical loss induced by their correlation.

Light curves describe temporal variations in the brightness of celestial objects. Learning robust representations of light curves is essential for large-scale automatic discovery in the dynamic universe, but existing time-series foundation models often struggle with the uneven sampling, complex noise, and wide range of physical timescales that characterize astronomical observations. We propose a domain-informed representation learning framework for irregular astronomical time series with Joint-Embedding predictive architecture (JEPA), combining semantics-preserving views, uncertainty-aware tokenization, and multi-view self-distillation. The encoders are trained with multi-view self-distillation using LeJEPA regularization on the LEAVES dataset and evaluated on the StarEmbed classification benchmark. On StarEmbed, our model outperforms hand-crafted features on 15 of 16 classification metrics. In few-shot linear probing, it achieves macro-F1 scores of 42.56 pm 7.21 with one sample per class and 63.58 pm 1.20 with 100 samples per class, consistently improving over hand-crafted features. Beyond variable-star classification, the learned representation supports similarity search, parameter estimation, and photometric zero-point drift detection. We further evaluate cross-domain adaptation on 12 heterogeneous irregular time-series datasets from PYRREGULAR, where the adapted variant matches or exceeds previous state-of-the-art performance on 5 datasets, compared with at most 3 wins by any single prior baseline. These results demonstrate that domain-informed multi-view self-distillation is an effective strategy for learning representations of irregular time series, while also highlighting that successful time-series representation learning requires domain-specific inductive biases rather than a universally optimal architecture.

Fluorescence LiDAR (FLiDAR), a Light Detection and Ranging (LiDAR) technology employed for distance and depth estimation across medical, automotive, and other fields, encounters significant computational challenges in scattering media. The complex nature of the acquired FLiDAR signal, particularly in such environments, makes isolating photon time-of-flight (related to target depth) and intrinsic fluorescence lifetime exceptionally difficult, thus limiting the effectiveness of current analytical and computational methodologies. To overcome this limitation, we present a Physics-Guided Mixture-of-Experts (MoE) framework tailored for specialized modeling of diverse temporal components. In contrast to the conventional MoE approaches our expert models are informed by underlying physics, such as the radiative transport equation governing photon propagation in scattering media. Central to our approach is EvidenceMoE, which integrates Evidence-Based Dirichlet Critics (EDCs). These critic models assess the reliability of each expert's output by providing per-expert quality scores and corrective feedback. A Decider Network then leverages this information to fuse expert predictions into a robust final estimate adaptively. We validate our method using realistically simulated Fluorescence LiDAR (FLiDAR) data for non-invasive cancer cell depth detection generated from photon transport models in tissue. Our framework demonstrates strong performance, achieving a normalized root mean squared error (NRMSE) of 0.030 for depth estimation and 0.074 for fluorescence lifetime.

Gravitational-wave data analysis relies on accurate and efficient methods to extract physical information from noisy detector signals, yet the increasing rate and complexity of observations represent a growing challenge. Deep learning provides a powerful alternative to traditional inference, but existing neural models typically lack the flexibility to handle variations in data analysis settings. Such variations accommodate imperfect observations or are required for specialized tests, and could include changes in detector configurations, overall frequency ranges, or localized cuts. We introduce a flexible transformer-based architecture paired with a training strategy that enables adaptation to diverse analysis settings at inference time. Applied to parameter estimation, we demonstrate that a single flexible model -- called Dingo-T1 -- can (i) analyze 48 gravitational-wave events from the third LIGO-Virgo-KAGRA Observing Run under a wide range of analysis configurations, (ii) enable systematic studies of how detector and frequency configurations impact inferred posteriors, and (iii) perform inspiral-merger-ringdown consistency tests probing general relativity. Dingo-T1 also improves median sample efficiency on real events from a baseline of 1.4% to 4.2%. Our approach thus demonstrates flexible and scalable inference with a principled framework for handling missing or incomplete data -- key capabilities for current and next-generation observatories.

We present the first joint analysis of the power spectrum and bispectrum using the Data Release 1 (DR1) of the Dark Energy Spectroscopic Instrument (DESI), focusing on Luminous Red Galaxies (LRGs) and quasars (QSOs) across a redshift range of 0.4leq zleq2.1. By combining the two- and three-point statistics, we are able to partially break the degeneracy between the logarithmic growth rate, f(z), and the amplitude of dark matter fluctuations, σ_s8(z), which cannot be measured separately in analyses that only involve the power spectrum. In comparison with the (fiducial) Planck ΛCDM cosmology we obtain f/f^fid={0.888_{-0.089}^{+0.186},0.977_{-0.220}^{+0.182},1.030_{-0.085}^{+0.368}}, σ_{s8}/σ^fid_s8={1.224_{-0.133}^{+0.091},1.071_{-0.163}^{+0.278},1.000_{-0.223}^{+0.088}} respectively for the three LRG redshift bins, corresponding to a cumulative 10.1\% constraint on f, and of 8.4\% on σ_s8, including the systematic error budget. The cumulative constraints for the ShapeFit compressed parameters from our joint power spectrum-bispectrum analysis are respectively σ_{α_iso}=0.9% (9\% improvement with respect to our power spectrum-only analysis); σ_{α_AP}=2.3% (no improvement with respect to power spectrum-only analysis, which is expected given that the bispectrum monopole has no significant anisotropic signal); σ_{fσ_s8}=5.1% (9\% improvement); σ_{m+n}=2.3% (11\% improvement). These results are fully consistent with the main DESI power spectrum analysis, demonstrating the robustness of the DESI cosmological constraints, and compatible with Planck ΛCDM cosmology.

We present new limits on an isotropic stochastic gravitational-wave background (GWB) using a six pulsar dataset spanning 18 yr of observations from the 2015 European Pulsar Timing Array data release. Performing a Bayesian analysis, we fit simultaneously for the intrinsic noise parameters for each pulsar, along with common correlated signals including clock, and Solar System ephemeris errors, obtaining a robust 95% upper limit on the dimensionless strain amplitude A of the background of A<3.0times 10^{-15} at a reference frequency of 1yr^{-1} and a spectral index of 13/3, corresponding to a background from inspiralling super-massive black hole binaries, constraining the GW energy density to Omega_gw(f)h^2 < 1.1times10^{-9} at 2.8 nHz. We also present limits on the correlated power spectrum at a series of discrete frequencies, and show that our sensitivity to a fiducial isotropic GWB is highest at a frequency of sim 5times10^{-9}~Hz. Finally we discuss the implications of our analysis for the astrophysics of supermassive black hole binaries, and present 95% upper limits on the string tension, Gmu/c^2, characterising a background produced by a cosmic string network for a set of possible scenarios, and for a stochastic relic GWB. For a Nambu-Goto field theory cosmic string network, we set a limit Gmu/c^2<1.3times10^{-7}, identical to that set by the {\it Planck} Collaboration, when combining {\it Planck} and high-ell Cosmic Microwave Background data from other experiments. For a stochastic relic background we set a limit of Omega^relic_gw(f)h^2<1.2 times10^{-9}, a factor of 9 improvement over the most stringent limits previously set by a pulsar timing array.

In recent years, the demand for hyperspectral imaging devices has grown significantly, driven by their ability of capturing high-resolution spectral information. Among the several possible optical designs for acquiring hyperspectral images, there is a growing interest in interferometric spectral imaging systems based on division of aperture. These systems have the advantage of capturing snapshot acquisitions while maintaining a compact design. However, they require a careful calibration to operate properly. In this work, we present the interferometer response characterization algorithm (IRCA), a robust three-step procedure designed to characterize the transmittance response of multi-aperture imaging spectrometers based on the interferometry of Fabry-Perot. Additionally, we propose a formulation of the image formation model for such devices suitable to estimate the parameters of interest by considering the model under various regimes of finesse. The proposed algorithm processes the image output obtained from a set of monochromatic light sources and refines the results using nonlinear regression after an ad-hoc initialization. Through experimental analysis conducted on four different prototypes from the Image SPectrometer On Chip (ImSPOC) family, we validate the performance of our approach for characterization. The associated source code for this paper is available at https://github.com/danaroth83/irca.

Light emission from galaxies exhibit diverse brightness profiles, influenced by factors such as galaxy type, structural features and interactions with other galaxies. Elliptical galaxies feature more uniform light distributions, while spiral and irregular galaxies have complex, varied light profiles due to their structural heterogeneity and star-forming activity. In addition, galaxies with an active galactic nucleus (AGN) feature intense, concentrated emission from gas accretion around supermassive black holes, superimposed on regular galactic light, while quasi-stellar objects (QSO) are the extreme case of the AGN emission dominating the galaxy. The challenge of identifying AGN and QSO has been discussed many times in the literature, often requiring multi-wavelength observations. This paper introduces a novel approach to identify AGN and QSO from a single image. Diffusion models have been recently developed in the machine-learning literature to generate realistic-looking images of everyday objects. Utilising the spatial resolving power of the Euclid VIS images, we created a diffusion model trained on one million sources, without using any source pre-selection or labels. The model learns to reconstruct light distributions of normal galaxies, since the population is dominated by them. We condition the prediction of the central light distribution by masking the central few pixels of each source and reconstruct the light according to the diffusion model. We further use this prediction to identify sources that deviate from this profile by examining the reconstruction error of the few central pixels regenerated in each source's core. Our approach, solely using VIS imaging, features high completeness compared to traditional methods of AGN and QSO selection, including optical, near-infrared, mid-infrared, and X-rays.

A sample of 60,410 bona fide optical quasars with astrometric proper motions in Gaia EDR3 and spectroscopic redshifts above 0.5 in an oval 8400 square degree area of the sky is constructed. Using orthogonal Zernike functions of polar coordinates, the proper motion fields are fitted in a weighted least-squares adjustment of the entire sample and of six equal bins of sorted redshifts. The overall fit with 37 Zernike functions reveals a statistically significant pattern, which is likely to be of instrumental origin. The main feature of this pattern is a chain of peaks and dips mostly in the R.A. component with an amplitude of 25~muas yr^{-1}. This field is subtracted from each of the six analogous fits for quasars grouped by redshifts covering the range 0.5 through 7.03, with median values 0.72, 1.00, 1.25, 1.52, 1.83, 2.34. The resulting residual patterns are noisier, with formal uncertainties up to 8~muas yr^{-1} in the central part of the area. We detect a single high-confidence Zernike term for R.A. proper motion components of quasars with redshifts around 1.52 representing a general gradient of 30 muas yr^{-1} over 150degr on the sky. We do not find any small- or medium-scale systemic variations of the residual proper motion field as functions of redshift above the 2.5,sigma significance level.

We present a web-based software tool, the Virtual Quantum Optics Laboratory (VQOL), that may be used for designing and executing realistic simulations of quantum optics experiments. A graphical user interface allows one to rapidly build and configure a variety of different optical experiments, while the runtime environment provides unique capabilities for visualization and analysis. All standard linear optical components are available as well as sources of thermal, coherent, and entangled Gaussian states. A unique aspect of VQOL is the introduction of non-Gaussian measurements using detectors modeled as deterministic devices that "click" when the amplitude of the light falls above a given threshold. We describe the underlying theoretical models and provide several illustrative examples. We find that VQOL provides a a faithful representation of many experimental quantum optics phenomena and may serve as both a useful instructional tool for students as well as a valuable research tool for practitioners.

We present warpax, an open-source, GPU-accelerated Python toolkit for observer-robust energy-condition verification of warp drive spacetimes, together with a benchmark application to six warp-drive geometries that demonstrates the methodology and produces new quantitative findings. Existing tools evaluate energy conditions for a finite sample of observer directions. warpax replaces discrete sampling with continuous, gradient-based optimization over the full timelike observer manifold, backed by Hawking--Ellis algebraic classification. At Type~I stress-energy points, which dominate all tested metrics, an algebraic eigenvalue check determines energy-condition satisfaction exactly, independent of any observer search. At non-Type~I points, the optimizer provides rapidity-capped diagnostics. Stress-energy tensors are computed from the Arnowitt--Deser--Misner metric via forward-mode automatic differentiation, eliminating finite-difference truncation error. We apply warpax to five warp drive metrics (Alcubierre, Lentz, Van~Den~Broeck, Nat'ario, Rodal) and one warp shell metric. For several metrics, the standard Eulerian-frame analysis misses a significant fraction of violated grid points; even where it identifies the correct violation set, observer optimization reveals violation magnitudes can be orders of magnitude larger. These results demonstrate that single-frame evaluation can systematically underestimate both the spatial extent and severity of energy-condition violations. Throughout, we distinguish the invariant energy density (eigenvalue of T^a{}b) from the observer-dependent T{ab},u^a u^b and the Eulerian projection. All results use subluminal bubble velocities; at superluminal speeds, the Alcubierre-family metrics develop signature changes outside our assumptions. warpax is freely available at https://github.com/anindex/warpax

As ground-based all-sky astronomical surveys will gather millions of images in the coming years, a critical requirement emerges for the development of fast deconvolution algorithms capable of efficiently improving the spatial resolution of these images. By successfully recovering clean and high-resolution images from these surveys, the objective is to deepen the understanding of galaxy formation and evolution through accurate photometric measurements. We introduce a two-step deconvolution framework using a Swin Transformer architecture. Our study reveals that the deep learning-based solution introduces a bias, constraining the scope of scientific analysis. To address this limitation, we propose a novel third step relying on the active coefficients in the sparsity wavelet framework. We conducted a performance comparison between our deep learning-based method and Firedec, a classical deconvolution algorithm, based on an analysis of a subset of the EDisCS cluster samples. We demonstrate the advantage of our method in terms of resolution recovery, generalisation to different noise properties, and computational efficiency. The analysis of this cluster sample not only allowed us to assess the efficiency of our method, but it also enabled us to quantify the number of clumps within these galaxies in relation to their disc colour. This robust technique that we propose holds promise for identifying structures in the distant universe through ground-based images.

A new golden age in astronomy is upon us, dominated by data. Large astronomical surveys are broadcasting unprecedented rates of information, demanding machine learning as a critical component in modern scientific pipelines to handle the deluge of data. The upcoming Legacy Survey of Space and Time (LSST) of the Vera C. Rubin Observatory will raise the big-data bar for time-domain astronomy, with an expected 10 million alerts per-night, and generating many petabytes of data over the lifetime of the survey. Fast and efficient classification algorithms that can operate in real-time, yet robustly and accurately, are needed for time-critical events where additional resources can be sought for follow-up analyses. In order to handle such data, state-of-the-art deep learning architectures coupled with tools that leverage modern hardware accelerators are essential. We showcase how the use of modern deep compression methods can achieve a 18times reduction in model size, whilst preserving classification performance. We also show that in addition to the deep compression techniques, careful choice of file formats can improve inference latency, and thereby throughput of alerts, on the order of 8times for local processing, and 5times in a live production setting. To test this in a live setting, we deploy this optimised version of the original time-series transformer, t2, into the community alert broking system of FINK on real Zwicky Transient Facility (ZTF) alert data, and compare throughput performance with other science modules that exist in FINK. The results shown herein emphasise the time-series transformer's suitability for real-time classification at LSST scale, and beyond, and introduce deep model compression as a fundamental tool for improving deploy-ability and scalable inference of deep learning models for transient classification.

The Legacy Survey of Space and Time (LSST) at the Vera C. Rubin Observatory will capture light curves (LCs) for 10 billion sources and produce millions of transient candidates per night, necessitating scalable, accurate, and efficient classification. To prepare the community for this scale of data, the Extended LSST Astronomical Time-Series Classification Challenge (ELAsTiCC) sought to simulate a diversity of LSST-like time-domain events. Using a small transformer-based model and refined light curve encoding logic, we present a new state of the art classification performance on ELAsTiCC, with 71.8% F1 on LC-only classifications, and 89.8% F1 on LC+metadata classifications. Previous state of the art was 65.5% F1 for LC-only, and for LC+metadata, 84% F1 with a different setup and 83.5% F1 with a directly comparable setup. Our model outperforms previous state-of-the-art models for fine-grained early detection at all time cutoffs, which should help prioritize candidate transients for follow-up observations. We demonstrate label-efficient training by removing labels from 90% of the training data (chosen uniformly at random), and compensate by leveraging regularization, bootstrap ensembling, and unsupervised pretraining. Even with only 10% of the labeled data, we achieve 67.4% F1 on LC-only and 87.1% F1 on LC+metadata, validating an approach that should help mitigate synthetic and observational data drift, and improve classification on tasks with less labeled data. We find that our base model is poorly calibrated via reliability diagrams, and correct it at a minimal cost to overall performance, enabling selections by classification precision. Finally, our GPU-optimized implementation is 9x faster than other state-of-the-art ELAsTiCC models, and can run inference at ~33000 LCs/s on a consumer-grade GPU, making it suitable for large-scale applications, and less expensive to train.

Optical coherence tomography (OCT) and scanning laser ophthalmoscopy (SLO) of the eye has become essential to ophthalmology and the emerging field of oculomics, thus requiring a need for transparent, reproducible, and rapid analysis of this data for clinical research and the wider research community. Here, we introduce OCTolyzer, the first open-source toolkit for retinochoroidal analysis in OCT/SLO data. It features two analysis suites for OCT and SLO data, facilitating deep learning-based anatomical segmentation and feature extraction of the cross-sectional retinal and choroidal layers and en face retinal vessels. We describe OCTolyzer and evaluate the reproducibility of its OCT choroid analysis. At the population level, metrics for choroid region thickness were highly reproducible, with a mean absolute error (MAE)/Pearson correlation for macular volume choroid thickness (CT) of 6.7mum/0.99, macular B-scan CT of 11.6mum/0.99, and peripapillary CT of 5.0mum/0.99. Macular choroid vascular index (CVI) also showed strong reproducibility, with MAE/Pearson for volume CVI yielding 0.0271/0.97 and B-scan CVI 0.0130/0.91. At the eye level, measurement noise for regional and vessel metrics was below 5% and 20% of the population's variability, respectively. Outliers were caused by poor-quality B-scans with thick choroids and invisible choroid-sclera boundary. Processing times on a laptop CPU were under three seconds for macular/peripapillary B-scans and 85 seconds for volume scans. OCTolyzer can convert OCT/SLO data into reproducible and clinically meaningful retinochoroidal features and will improve the standardisation of ocular measurements in OCT/SLO image analysis, requiring no specialised training or proprietary software to be used. OCTolyzer is freely available here: https://github.com/jaburke166/OCTolyzer.

High Power Laser (HPL) systems operate in the attoseconds regime -- the shortest timescale ever created by humanity. HPL systems are instrumental in high-energy physics, leveraging ultra-short impulse durations to yield extremely high intensities, which are essential for both practical applications and theoretical advancements in light-matter interactions. Traditionally, the parameters regulating HPL optical performance have been manually tuned by human experts, or optimized using black-box methods that can be computationally demanding. Critically, black box methods rely on stationarity assumptions overlooking complex dynamics in high-energy physics and day-to-day changes in real-world experimental settings, and thus need to be often restarted. Deep Reinforcement Learning (DRL) offers a promising alternative by enabling sequential decision making in non-static settings. This work explores the feasibility of applying DRL to HPL systems, extending the current research by (1) learning a control policy relying solely on non-destructive image observations obtained from readily available diagnostic devices, and (2) retaining performance when the underlying dynamics vary. We evaluate our method across various test dynamics, and observe that DRL effectively enables cross-domain adaptability, coping with dynamics' fluctuations while achieving 90\% of the target intensity in test environments.

The illumination of the accretion disks is frequently studied assuming that the incident X-ray flux is a point-like source. The approach is referred as lamppost model.The most recent computations of the X-ray reprocessing by the disk take into account the departure from the simple lamppost models. However, in computations of the incident flux thermalization and subsequent re-emission in the optical-UV band the lamppost approximation is most frequently assumed. We test if the UV-optical reverberation mapping and time delay measurements are sensitive to this assumption. We assume that the incident radiation originates from a region extended along the symmetry axis. To model this, we adopt a simple setup by representing the emission as two lamps irradiating the disk simultaneously from two different heights. We then compare the resulting predictions with those obtained for a single lamppost located at an intermediate height. We show at the basis of the transfer function that the deviation of the wavelength-dependent delay curve shows at most a difference of 20% in comparison to a single lamppost, assuming the black hole mass of 10^8 M_{odot}, Eddington ratio 1, and the location of the lamps at 5 and 100 rg. The maximum deviation happens for the lamp luminosity ratio sim3. When simulating light curves for a two-lamp setup and a standard lamppost with the same black hole mass and a sampling rate of 0.1 days, we find no measurable differences in the ICCF profiles between the two setups. Larger black hole mass and considerably lower Eddington ratio would allow to see larger differences between a single lamppost and a two-lampost model. UV/optical reverberation mapping is not very sensitive to the vertical extension of the corona.

In the axion model, electromagnetic waves interacting with axions induce frequency-dependent time delays, determined by the axion mass and decay constant. These small delays are difficult to detect, making traditional methods ineffective. To address this, we computed time delays for various parameters and found a prominent dispersion signal when the wave frequency equals half the axion mass. Based on this, we developed a machine learning-based pipeline, achieving 95\% classification accuracy and demonstrating strong detection capability in low signal-to-noise data. Applying this to PSR J1933-6211, we found no axion-induced delays within current sensitivity limits. While existing constraints are limited by atomic clock resolution in radio telescopes, future advances in optical clocks and broader bandwidths will enable more extensive searches. In particular, combining high-precision optical clocks with next-generation radio telescopes, such as the Qitai Radio Telescope, could improve decay constant constraints by four orders of magnitude for axion masses in the 10^{-6} sim 10^{-4} eV range.

Short-pulse, ultra high-intensity lasers have opened new regimes for studying fusion plasmas and creating novel ultra-short ion beams and neutron sources. Diagnosing the plasma in these experiments is important for optimizing the fusion yield but difficult due to the picosecond time scales, 10s of micron-cubed volumes and high densities. We propose to use the yields of photons and neutrons produced by parallel reactions involving the same reactants to diagnose the plasma conditions and predict the yields of specific reactions of interest. In this work, we focus on verifying the yield of the high-interest aneutronic proton-boron fusion reaction ^{11}{B}(p,2α){}^4{He}, which is difficult to measure directly due to the short stopping range of the produced αs in most materials. We identify promising photon-producing reactions for this purpose and compute the ratios of the photon yield to the α yield as a function of plasma parameters. In beam fusion experiments, the {}^{11}{C} yield is an easily-measurable observable to verify the α yield. In light of our results, improving and extending measurements of the cross sections for these parallel reactions are important steps to gaining greater control over these laser-driven fusion plasmas.

We present an improved version of the 3D Monte Carlo radiative transfer code POSSIS to model kilonovae from neutron star mergers, wherein nuclear heating rates, thermalization efficiencies and wavelength-dependent opacities depend on local properties of the ejecta and time. Using an axially-symmetric two-component ejecta model, we explore how simplistic assumptions on heating rates, thermalization efficiencies and opacities often found in the literature affect kilonova spectra and light curves. Specifically, we compute five models: one (FIDUCIAL) with an appropriate treatment of these three quantities, one (SIMPLE-HEAT) with uniform heating rates throughout the ejecta, one (SIMPLE-THERM) with a constant and uniform thermalization efficiency, one (SIMPLE-OPAC) with grey opacities and one (SIMPLE-ALL) with all these three simplistic assumptions combined. We find that deviations from the FIDUCIAL model are of several (sim1-10) magnitudes and are generally larger for the SIMPLE-OPAC and SIMPLE-ALL compared to the SIMPLE-THERM and SIMPLE-HEAT models. The discrepancies generally increase from a face-on to an edge-on view of the system, from early to late epochs and from infrared to ultraviolet/optical wavelengths. Our work indicates that kilonova studies using either of these simplistic assumptions ought to be treated with caution and that appropriate systematic uncertainties ought to be added to kilonova light curves when performing inference on ejecta parameters.

Nowadays, with the rising number of sensors in sectors such as healthcare and industry, the problem of multivariate time series classification (MTSC) is getting increasingly relevant and is a prime target for machine and deep learning approaches. Their expanding adoption in real-world environments is causing a shift in focus from the pursuit of ever-higher prediction accuracy with complex models towards practical, deployable solutions that balance accuracy and parameters such as prediction speed. An MTSC model that has attracted attention recently is ROCKET, based on random convolutional kernels, both because of its very fast training process and its state-of-the-art accuracy. However, the large number of features it utilizes may be detrimental to inference time. Examining its theoretical background and limitations enables us to address potential drawbacks and present LightWaveS: a framework for accurate MTSC, which is fast both during training and inference. Specifically, utilizing wavelet scattering transformation and distributed feature selection, we manage to create a solution that employs just 2.5% of the ROCKET features, while achieving accuracy comparable to recent MTSC models. LightWaveS also scales well across multiple compute nodes and with the number of input channels during training. In addition, it can significantly reduce the input size and provide insight to an MTSC problem by keeping only the most useful channels. We present three versions of our algorithm and their results on distributed training time and scalability, accuracy, and inference speedup. We show that we achieve speedup ranging from 9x to 53x compared to ROCKET during inference on an edge device, on datasets with comparable accuracy.

With the successful launch and commissioning of JWST we are now able to routinely spectroscopically probe the rest-frame optical emission of galaxies at z>6 for the first time. Amongst the most useful spectral diagnostics used in the optical is the Balmer/4000~\AA\ break; this is, in principle, a diagnostic of the mean ages of composite stellar populations. However, the Balmer break is also sensitive to the shape of the star formation history, the stellar (and gas) metallicity, the presence of nebular continuum emission, and dust attenuation. In this work we explore the origin of the Balmer/4000~\AA\ break using the SYNTHESIZER synthetic observations package. We then make predictions of the Balmer/4000~\AA\ break using the First Light and Reionisation Epoch Simulations (FLARES) at 5<z<10. We find that the average break strength weakly correlates with stellar mass and rest-frame far-UV luminosity, but that this is predominantly driven by dust attenuation. We also find that break strength provides a weak diagnostic of the age but performs better as a means to constrain star formation and stellar mass, alongside the UV and optical luminosity, respectively.

We use the First Light And Reionisation Epoch Simulations (FLARES) to study the evolution of the rest-frame ultraviolet (UV) and far-infrared (FIR) sizes for a statistical sample of massive (gtrsim10^{9}M_{odot}) high redshift galaxies (z in [5,10]). Galaxies are post-processed using the SKIRT radiative transfer code, to self-consistently obtain the full spectral energy distribution and surface brightness distribution. We create mock observations of the galaxies for the Near Infrared Camera (NIRCam) to study the rest-frame UV 1500 xC5 morphology. We also generate mock rest-frame FIR (50 mum) photometry and mock ALMA (158 mum) (0.01"-0.03" and approx0.3" angular resolution) observations to study the dust-continuum. We find the effect of dust on observed sizes reduces with increasing wavelength from the UV to optical (sim0.6 times the UV at 0.4mum), with no evolution in FIR sizes. Observed sizes vary within 0.4-1.2 times the intrinsic sizes at different signal to noise ratios (SNR = 5-20) across redshifts. The effect of PSF and noise makes bright structures prominent, whereas fainter regions blend with noise, leading to an underestimation (factor of 0.4-0.8) of sizes at SNR=5. At SNR=15-20, the underestimation reduces (factor of 0.6-0.9) at z=5-8 but due to PSF, at z=9-10, bright cores are dominant, resulting in an overestimation (factor of 1.0-1.2). For ALMA, low resolution sizes are effected by noise which acts as extended emission. The size evolution in UV broadly agrees with current observational samples and other simulations. This work is one of the first to analyse the panchromatic sizes of a statistically significant sample of simulated high-redshift galaxies, complementing a growing body of research highlighting the importance of conducting an equivalent comparison between observed galaxies and their simulated counterparts in the early Universe.

We use four observations with the European VLBI network to measure the first precise radio parallax of the Crab Pulsar. We found two in-beam extragalactic sources just outside the Crab Nebula, with one bright enough to use as a background reference source in our data. We use the Crab Pulsar's giant pulses to determine fringe and bandpass calibration solutions, which greatly improved the sensitivity and reliability of our images and allowed us to determine precise positional offsets between the pulsar and the background source. From those offsets, we determine a parallax of π=0.53pm0.06;mas and proper motion of (μ_α,μ_δ)=(-11.34pm0.06,2.65pm0.14);mas;yr^{-1}, yielding a distance of d=1.90^{+0.22}_{-0.18};kpc and transverse velocity of v_{perp}=104^{+13}_{-11};km;s^{-1}. These results are consistent with the Gaia 3 measurements, and open up the possibility of far more accurate astrometry with further VLBI observations.

We present synthetic light curves and spectra from three-dimensional (3D) Monte Carlo radiative transfer simulations based on a 3D core-collapse supernova explosion model of an ultra-stripped 3.5,M_{odot} progenitor. Our calculations predict a fast and faint transient with Delta m_{15} sim 1- 2,mag and peak bolometric luminosity between -15.3,mag and -16.4,mag. Due to a large-scale unipolar asymmetry in the distribution of ^{56}Ni, there is a pronounced viewing-angle dependence with about 1,mag difference between the directions of highest and lowest luminosity. The predicted spectra for this rare class of explosions do not yet match any observed counterpart. They are dominated by prominent Mg~II lines, but features from O, C, Si, and Ca are also found. In particular, the O~I line at 7{774} appears as a blended feature together with Mg~II emission. Our model is not only faster and fainter than the observed Ib/c supernova population, but also shows a correlation between higher peak luminosity and larger Delta m_{15} that is not present in observational samples. A possible explanation is that the unusually small ejecta mass of our model accentuates the viewing-angle dependence of the photometry. We suggest that the viewing-angle dependence of the photometry may be used to constrain asymmetries in explosion models of more typical stripped-envelope supernova progenitors in future.

We present a comprehensive evaluation of proprietary and open-weights large language models using the first astronomy-specific benchmarking dataset. This dataset comprises 4,425 multiple-choice questions curated from the Annual Review of Astronomy and Astrophysics, covering a broad range of astrophysical topics. Our analysis examines model performance across various astronomical subfields and assesses response calibration, crucial for potential deployment in research environments. Claude-3.5-Sonnet outperforms competitors by up to 4.6 percentage points, achieving 85.0% accuracy. For proprietary models, we observed a universal reduction in cost every 3-to-12 months to achieve similar score in this particular astronomy benchmark. Open-source models have rapidly improved, with LLaMA-3-70b (80.6%) and Qwen-2-72b (77.7%) now competing with some of the best proprietary models. We identify performance variations across topics, with non-English-focused models generally struggling more in exoplanet-related fields, stellar astrophysics, and instrumentation related questions. These challenges likely stem from less abundant training data, limited historical context, and rapid recent developments in these areas. This pattern is observed across both open-weights and proprietary models, with regional dependencies evident, highlighting the impact of training data diversity on model performance in specialized scientific domains. Top-performing models demonstrate well-calibrated confidence, with correlations above 0.9 between confidence and correctness, though they tend to be slightly underconfident. The development for fast, low-cost inference of open-weights models presents new opportunities for affordable deployment in astronomy. The rapid progress observed suggests that LLM-driven research in astronomy may become feasible in the near future.

In our previous paper, we developed a technique for identifying pulsar candidates in interferometric radio images using their distinctive scintillation signatures. Building on this technique, the present study simulates a pulsar population using the PsrPopPy Python module to investigate the technique's limitations and detection capabilities. Among pulsars detectable exclusively by this technique, 50% have duty cycles exceeding the mean value of 0.09 observed in time-domain detections. Our pulsar population simulations revealed a set of observational parameters that optimize pulsar detection. An observation frequency of ~ 1420 MHz and a channel width of ~10 kHz emerge as the optimal configuration to maximize the pulsar detection efficiency. By applying a scintillation-based technique to future radio telescopes like DSA-2000, we can detect 56% of normal pulsars and 84% of MSPs in addition to those detected using non-imaging, time-domain surveys. These detected pulsars cannot be verified by time-domain searches.

We show that pairs of widely separated interferometers are advantageous for measuring the Stokes parameter V of a stochastic background of gravitational waves. This parameter characterizes asymmetry of amplitudes of right- and left-handed waves and generation of the asymmetry is closely related to parity violation in the early universe. The advantageous pairs include LIGO(Livingston)-LCGT and AIGO-Virgo that are relatively insensitive to Omega_GW (the simple intensity of the background). Using at least three detectors, information of the intensity Omega_GW and the degree of asymmetry V can be separately measured.

Blazars, a class of active galactic nuclei (AGN) powered by supermassive black holes, are known for their remarkable variability across multiple timescales and wavelengths. With advancements in both ground- and space-based telescopes, our understanding of AGN central engines has significantly improved. However, the mechanisms driving this variability remain elusive, and continue to fascinate both theorists and observers alike. The primary objective of this study is to constrain the X-ray variability properties of the TeV blazar PKS 2155-304. We conduct a comprehensive X-ray spectral and timing analysis, focusing on both long-term and intra-day variability. This analysis uses data from 22 epochs of XMM-Newton EPIC-pn observations, collected over 15 years (2000-2014). To investigate the variability of the source, we applied both timing and spectral analyses. For the timing analysis, we estimated fractional variability, variability amplitude, minimum variability timescales, flux distribution, and power spectral density (PSD). In the spectral analysis, we fitted the X-ray spectra using power-law, log-parabola, and broken power-law (BPL) models to determine the best-fitting parameters. Additionally, we studied the hardness ratio (HR). We observed moderate intra-day variability in most of the light curves. Seven out of the twenty-two observations showed a clear bimodal flux distribution, indicating the presence of two distinct flux states. Our analysis revealed a variable power-law PSD slope. Most HR plots did not show significant variation with flux, except for one observation (OBSID 0124930501), where HR increased with flux (Count/s). The fitted X-ray spectra favored the BPL model for the majority of observations. The findings of this work shed light on the intraday variability of blazars, providing insights into the non-thermal jet processes that drive the observed flux variations.

The observation of the shadow of the supermassive black hole M87^{*} by the Event Horizon Telescope (EHT) is sensitive to the spacetime geometry near the circular photon orbit and beyond, and it thus has the potential to test general relativity in the strong field regime. Obstacles to this program, however, include degeneracies between putative deviations from general relativity and both the description of the accretion flow and the uncertainties on "calibration parameters", such as e.g. the mass and spin of the black hole. In this work, we introduce a formalism, based on a principal component analysis, capable of reconstructing the black hole metric (i.e. the "signal") in an agnostic way, while subtracting the "foreground" due to the uncertainties in the calibration parameters and the modelling of the accretion flow. We apply our technique to simulated mock data for spherically symmetric black holes surrounded by a thick accretion disk. We show that separation of signal and foreground may be possible with next generation EHT-like experiments.

The angular power spectrum is a gauge-independent observable that is in principle the natural tool for analysing galaxy number counts. In practice, the problem is that the computational requirements for next-generation spectroscopic surveys such as Euclid and the Square Kilometre Array are currently unfeasible. We propose a new method to save computational time for spectroscopic angular power spectra. This hybrid method is modelled on the Fourier power spectrum approach of treating relatively thick redshift bins (redshift width ~0.1) as separate surveys. In the hybrid method, each thick bin is further subdivided into thin bins (redshift width ~0.01); all the correlations within each thick bin are computed, while cross-bin correlations beyond the thick bins are neglected. Constraints on cosmological parameters from the hybrid method are comparable to those from the standard galaxy power spectrum analysis - but they have the advantage that cosmic evolution, wide-angle and lensing effects are naturally included, while no Alcock-Paczynski correction is needed. The hybrid method delivers much tighter constraints than a 2D tomographic approach that is typical for photometric surveys, which considers only thick bins and the correlations between them. Furthermore, for standard cosmological parameters our method is not biased by neglecting the effects of lensing on number counts, while the tomographic method is strongly biased.

The need to estimate the speed of road vehicles has become increasingly important in the field of video forensics, particularly with the widespread deployment of CCTV cameras worldwide. Despite the development of various approaches, the accuracy of forensic speed estimation from real-world footage remains highly dependent on several factors, including camera specifications, acquisition methods, spatial and temporal resolution, compression methods, and scene perspective, which can significantly influence performance. In this paper, we introduce ForeSpeed, a comprehensive dataset designed to support the evaluation of speed estimation techniques in real-world scenarios using CCTV footage. The dataset includes recordings of a vehicle traveling at known speeds, captured by three digital and three analog cameras from two distinct perspectives. Real-world road metrics are provided to enable the restoration of the scene geometry. Videos were stored with multiple compression factors and settings, to simulate real world scenarios in which export procedures are not always performed according to forensic standards. Overall, ForeSpeed, includes a collection of 322 videos. As a case study, we employed the ForeSpeed dataset to benchmark a speed estimation algorithm available in a commercial product (Amped FIVE). Results demonstrate that while the method reliably estimates average speed across various conditions, its uncertainty range significantly increases when the scene involves strong perspective distortion. The ForeSpeed dataset is publicly available to the forensic community, with the aim of facilitating the evaluation of current methodologies and inspiring the development of new, robust solutions tailored to collision investigation and forensic incident analysis.

We report an optical method of generating arbitrary polarization states by manipulating the thicknesses of a pair of uniaxial birefringent plates, the optical axes of which are set at a crossing angle of {\pi}/4. The method has the remarkable feature of being able to generate a distribution of arbitrary polarization states in a group of highly discrete spectra without spatially separating the individual spectral components. The target polarization-state distribution is obtained as an optimal solution through an exploration. Within a realistic exploration range, a sufficient number of near-optimal solutions are found. This property is also reproduced well by a concise model based on a distribution of exploration points on a Poincar\'e sphere, showing that the number of near-optimal solutions behaves according to a power law with respect to the number of spectral components of concern. As a typical example of an application, by applying this method to a set of phase-locked highly discrete spectra, we numerically demonstrate the continuous generation of a vector-like optical electric field waveform, the helicity of which is alternated within a single optical cycle in the time domain.

This paper presents the specification, design, and development of the Visible Camera (VIS) on the ESA Euclid mission. VIS is a large optical-band imager with a field of view of 0.54 deg^2 sampled at 0.1" with an array of 609 Megapixels and spatial resolution of 0.18". It will be used to survey approximately 14,000 deg^2 of extragalactic sky to measure the distortion of galaxies in the redshift range z=0.1-1.5 resulting from weak gravitational lensing, one of the two principal cosmology probes of Euclid. With photometric redshifts, the distribution of dark matter can be mapped in three dimensions, and, from how this has changed with look-back time, the nature of dark energy and theories of gravity can be constrained. The entire VIS focal plane will be transmitted to provide the largest images of the Universe from space to date, reaching m_AB>24.5 with S/N >10 in a single broad I_E~(r+i+z) band over a six year survey. The particularly challenging aspects of the instrument are the control and calibration of observational biases, which lead to stringent performance requirements and calibration regimes. With its combination of spatial resolution, calibration knowledge, depth, and area covering most of the extra-Galactic sky, VIS will also provide a legacy data set for many other fields. This paper discusses the rationale behind the VIS concept and describes the instrument design and development before reporting the pre-launch performance derived from ground calibrations and brief results from the in-orbit commissioning. VIS should reach fainter than m_AB=25 with S/N>10 for galaxies of full-width half-maximum of 0.3" in a 1.3" diameter aperture over the Wide Survey, and m_AB>26.4 for a Deep Survey that will cover more than 50 deg^2. The paper also describes how VIS works with the other Euclid components of survey, telescope, and science data processing to extract the cosmological information.

The lack of self-correcting codes hiders the development of boson sampling to be large-scale and robust. Therefore, it is important to know the noise levels in order to cautiously demonstrate the quantum computational advantage or realize certain tasks. Based on those statistical benchmark methods such as the correlators and the clouds, which are initially proposed to discriminate boson sampling and other mockups, we quantificationally evaluate noises of photon partial distinguishability and photon loss compensated by dark counts. This is feasible owing to the fact that the output distribution unbalances are suppressed by noises, which are actually results of multi-photon interferences. This is why the evaluation performance is better when high order correlators or corresponding clouds are employed. Our results indicate that the statistical benchmark methods can also work in the task of evaluating noises of boson sampling.

Time series data from observations of black hole ringdown gravitational waves are often analyzed in the time domain by using damped sinusoid models with acyclic boundary conditions. Data conditioning operations, including downsampling, filtering, and the choice of data segment duration, reduce the computational cost of such analyses and can improve numerical stability. Here we analyze simulated damped sinsuoid signals to illustrate how data conditioning operations, if not carefully applied, can undesirably alter the analysis' posterior distributions. We discuss how currently implemented downsampling and filtering methods, if applied too aggressively, can introduce systematic errors and skew tests of general relativity. These issues arise because current downsampling and filtering methods do not operate identically on the data and model. Alternative downsampling and filtering methods which identically operate on the data and model may be achievable, but we argue that the current operations can still be implemented safely. We also show that our preferred anti-alias filtering technique, which has an instantaneous frequency-domain response at its roll-off frequency, preserves the structure of posterior distributions better than other commonly used filters with transient frequency-domain responses. Lastly, we highlight that exceptionally long data segments may need to be analyzed in cases where thin lines in the noise power spectral density overlap with central signal frequencies. Our findings may be broadly applicable to any analysis of truncated time domain data with acyclic boundary conditions.

Wavefront shaping is a technique for directing light through turbid media. The theoretical aspects of wavefront shaping are well understood, and under near-ideal experimental conditions, accurate predictions for the expected signal enhancement can be given. In practice, however, there are many experimental factors that negatively affect the outcome of the experiment. Here, we present a comprehensive overview of these experimental factors, including the effect of sample scattering properties, noise, and response of the spatial light modulator. We present simple means to identify experimental imperfections and to minimize their negative effect on the outcome of the experiment. This paper is accompanied by Python code for automatically quantifying experimental problems using the OpenWFS framework for running and simulating wavefront shaping experiments.

The binding energies of the five bound rotational levels J=0-4 in the highest vibrational level v=14 in the X^1Sigma_g^+ ground electronic state of H_2 were measured in a three-step ultraviolet-laser experiment. Two-photon UV-photolysis of H_2S produced population in these high-lying bound states, that were subsequently interrogated at high precision via Doppler-free spectroscopy of the F^1Sigma_g^+ - X^1Sigma_g^+ system. A third UV-laser was used for detection through auto-ionizing resonances. The experimentally determined binding energies were found to be in excellent agreement with calculations based on non-adiabatic perturbation theory, also including relativistic and quantum electrodynamical contributions. The s-wave scattering length of the H + H system is derived from the binding energy of the last bound J=0 level via a direct semi-empirical approach, yielding a value of a_s = 0.2724(5) a_0, in good agreement with a result from a previously followed theoretical approach. The subtle effect of the malpha^4 relativity contribution to a_s was found to be significant. In a similar manner a value for the p-wave scattering volume is determined via the J=1 binding energy yielding a_p = -134.0000(6) a_0^3. The binding energy of the last bound state in H_2, the (v=14, J=4) level, is determined at 0.023(4) cm^{-1}, in good agreement with calculation. The effect of the hyperfine substructure caused by the two hydrogen atoms at large internuclear separation, giving rise to three distinct dissociation limits, is discussed.

To meet the challenges of high-resolution molecular spectroscopy, increasingly sophisticated spectroscopic techniques were developed. For a long time FTIR and laser-based spectroscopies were used for these studies. The recent development of dual-comb spectroscopy at high-resolution makes this technique a powerful tool for gas phase studies. We report on the use and characterization of the IRis-F1, a tabletop mid-infrared dual-comb spectrometer, in the newly developed step-sweep mode. The resolution of the wavenumber axis is increased by step-wise tuning (interleaving) and accurate measurement of the laser center wavelength and repetition frequency. Doppler limited measurements of N2O and CH4 reveal a wavenumber accuracy of 1E-4 cm-1 on the covered range of > 50 cm-1. Measured half-widths of absorption lines show no systematic broadening, indicating a negligible instrument response function. Finally, measurements of nitrogen pressure broadening coefficients in the v4 band of methane show that quantum cascade laser dual-comb spectroscopy in step-sweep mode is well adapted for measurements of precision spectroscopic data, in particular line shape parameters.

Attosecond pulses provide unique opportunities for studies of time-resolved electron dynamics. However, focusing these pulses, typically ranging from the vacuum ultraviolet to the soft-X-ray region, remains challenging. Conventional refractive lenses are not suitable owing to the large dispersion and the strong absorption, while reflective optics, despite avoiding these issues, still lead to high losses. Here we propose a tunable plasma lens capable of focusing attosecond pulses, and experimentally demonstrate focusing of these pulses at extreme-ultraviolet photon energies around 20 eV and 80 eV. A key advantage is its compatibility with nonlinear frequency conversion processes like high-harmonic generation. The different focusing properties of the fundamental light and the generated harmonic frequencies allow for efficient separation of these components. Consequently, the transmission of high-harmonic generation beamlines can be increased to more than 80%, making this approach highly suitable for photon-demanding applications.

Using observations from GMRT and FAST, we conducted multi-wavelength studies on PSR J1948+3540 and analyzed its intensity modulation characteristics in detail. We found that the intensity modulation of this pulsar exhibits broad low-frequency modulation features. The modulation frequency/period is time-dependent, but the dominant modulation component varies with the observing frequency. Specifically, at low frequencies, the modulation is dominated by the first half of the middle component, while at high frequencies, it is dominated by the second half of the middle component. Spectral analysis revealed that the intensities of the leading and trailing components vary with the observing frequency, but the middle component does not change significantly. Besides, the polarization analyses reveal that the peak of the radiation intensity is located in the latter half of the middle component, whereas the linear polarization is dominant in the former half. However, due to the low degree of linear polarization, the change of the dominant modulation component with the observed frequency is not caused by the variation in linear polarization. The phenomenon of the dominant modulation component varying with observing frequency has not been reported before and remains difficult to understand within the current theoretical framework.

Indirect Time of Flight LiDARs can indirectly calculate the scene's depth from the phase shift angle between transmitted and received laser signals with amplitudes modulated at a predefined frequency. Unfortunately, this method generates ambiguity in calculated depth when the phase shift angle value exceeds 2pi. Current state-of-the-art methods use raw samples generated using two distinct modulation frequencies to overcome this ambiguity problem. However, this comes at the cost of increasing laser components' stress and raising their temperature, which reduces their lifetime and increases power consumption. In our work, we study two different methods to recover the entire depth range of the LiDAR using fewer raw data sample shots from a single modulation frequency with the support of sensor's gray scale output to reduce the laser components' stress and power consumption.

A sizeable fraction of gamma-ray burst (GRB) light curves (LCs) features a sequence of peaks, which holds information on the unknown way energy is dissipated into gamma-rays over time. Traditional searches for periodic signals in GRB LCs turned out to be inconclusive, partly because they are challenging as a consequence of the short-lived, coloured-noise, and non-stationary nature of the LCs themselves. Yet, recent claims have revived the issue. We searched for periodic components in GRB LCs through a new approach to GRBs, that avoids most of the issues faced by traditional techniques. We identified peaks through a well tested algorithm and selected GRBs with at least 10 peaks out of 5 GRB catalogues (Swift/BAT, CGRO/BATSE, Fermi/GBM, Insight-HXMT, BeppoSAX/GRBM). Each GRB was simply treated as a discrete point process, whose realisation coincides with the sequence of peak times. We searched for possible periodic recurrences based on the multinomial distribution, after accounting for the clustering of peaks due to the non-stationarity of the GRB signals. The best candidate has a p-value of 3e-4 that there is no periodic recurrence. However, accounting for the multiple trials of 555 searched GRBs, its statistical significance is demoted to 17%. The overall distribution of the p-values obtained for all GRBs is compatible with a uniform distribution in [0,1]. We found no robust evidence for multi-peaked GRBs with periodic recurrences. We can exclude that a sizeable fraction (>~ 0.75) of peaks of each GRB with at least 10 peaks are periodic. While our result does not necessarily clash with claimed periodicities based on Fourier techniques, it constrains the putative recurrent behaviour, which would not manifest itself through the sequence of peaks, but, evidently, in a more elusive way.

Gamma-ray bursts (GRBs) associated with gravitational wave events are, and will likely continue to be, viewed at a larger inclination than GRBs without gravitational wave detections. As demonstrated by the afterglow of GW170817, this requires an extension of the common GRB afterglow models which typically assume emission from an on-axis top hat jet. We present a characterization of the afterglows arising from structured jets, providing a framework covering both successful and choked jets. We compute new closure relations for structured jets and compare with the established relations for energy injection and refreshed shock models. The temporal slope before the jet break is found to be a simple function of the ratio between the viewing angle and effective opening angle of the jet. A numerical model to calculate synthetic light curves and spectra is publicly available as the open source Python package afterglowpy.

The recent detection of nanohertz stochastic gravitational-wave backgrounds (SGWBs) by pulsar timing arrays (PTAs) promises unique insights into astrophysical and cosmological origins. However, traditional Markov Chain Monte Carlo (MCMC) approaches become prohibitively expensive for large datasets. We employ a normalizing flow (NF)-based machine learning framework to accelerate Bayesian inference in PTA analyses. For the first time, we perform Bayesian model comparison across SGWB source models in the framework of machine learning by training NF architectures on the PTA dataset (NANOGrav 15-year) and enabling direct evidence estimation via learned harmonic mean estimators. Our examples include 10 conventional SGWB source models such as supermassive black hole binaries, power-law spectrum, cosmic strings, domain walls, scalar-induced GWs, first-order phase transitions, and dual scenario/inflationary gravitational wave. Our approach jointly infers 20 red noise parameters and 2 SGWB parameters per model in sim 20\,hours (including training), compared to sim 10\,days with MCMC. Critically, the NF method preserves rigorous model selection accuracy, with small Hellinger distances (lesssim 0.3) relative to MCMC posteriors, and reproduces MCMC-based Bayes factors across all tested scenarios. This scalable technique for SGWB source comparison will be essential for future PTA expansions and next-generation arrays such as the SKA, offering orders-of-magnitude efficiency gains without sacrificing physical interpretability.

This work, together with its companion paper, Secco and Samuroff et al. (2021), presents the Dark Energy Survey Year 3 cosmic shear measurements and cosmological constraints based on an analysis of over 100 million source galaxies. With the data spanning 4143 deg^2 on the sky, divided into four redshift bins, we produce the highest significance measurement of cosmic shear to date, with a signal-to-noise of 40. We conduct a blind analysis in the context of the ΛCDM model and find a 3% constraint of the clustering amplitude, S_8equiv σ_8 (Ω_{rm m}/0.3)^{0.5} = 0.759^{+0.025}_{-0.023}. A ΛCDM-Optimized analysis, which safely includes smaller scale information, yields a 2% precision measurement of S_8= 0.772^{+0.018}_{-0.017} that is consistent with the fiducial case. The two low-redshift measurements are statistically consistent with the Planck Cosmic Microwave Background result, however, both recovered S_8 values are lower than the high-redshift prediction by 2.3σ and 2.1σ (p-values of 0.02 and 0.05), respectively. The measurements are shown to be internally consistent across redshift bins, angular scales and correlation functions. The analysis is demonstrated to be robust to calibration systematics, with the S_8 posterior consistent when varying the choice of redshift calibration sample, the modeling of redshift uncertainty and methodology. Similarly, we find that the corrections included to account for the blending of galaxies shifts our best-fit S_8 by 0.5σ without incurring a substantial increase in uncertainty. We examine the limiting factors for the precision of the cosmological constraints and find observational systematics to be subdominant to the modeling of astrophysics. Specifically, we identify the uncertainties in modeling baryonic effects and intrinsic alignments as the limiting systematics.

The inspiral, merger, and ringdown of Massive Black Hole Binaries (MBHBs) is one the main sources of Gravitational Waves (GWs) for the future Laser Interferometer Space Antenna (LISA), an ESA-led mission in the implementation phase. It is expected that LISA will detect these systems throughout the entire observable universe. Robust and efficient data analysis algorithms are necessary to detect and estimate physical parameters for these systems. In this work, we explore the application of Sequential Neural Likelihood, a simulation-based inference algorithm, to detect and characterize MBHB GW signals in synthetic LISA data. We describe in detail the different elements of the method, their performance and possible alternatives that can be used to enhance the performance. Instead of sampling from the conventional likelihood function, which requires a forward simulation for each evaluation, this method constructs a surrogate likelihood that is ultimately described by a neural network trained from a dataset of simulations of the MBHB signals and noise. One important advantage of this method is that, given that the likelihood is independent of the priors, we can iteratively train models that target specific observations in a fraction of the time and computational cost that other traditional and machine learning-based strategies would require. Because of the iterative nature of the method, we are able to train models to obtain qualitatively similar posteriors with less than 2\% of the simulator calls that Markov Chain Monte Carlo methods would require. We compare these posteriors with those obtained from Markov Chain Monte Carlo techniques and discuss the differences that appear, in particular in relation with the important role that data compression has in the modular implementation of the method that we present. We also discuss different strategies to improve the performance of the algorithms.

Phenomenological characteristics of the sample of the Algol-type stars are revised using a recently developed NAV ("New Algol Variable") algorithm (2012Ap.....55..536A, 2012arXiv 1212.6707A) and compared to that obtained using common methods of Trigonometric Polynomial Fit (TP) or local Algebraic Polynomial (A) fit of a fixed or (alternately) statistically optimal degree (1994OAP.....7...49A, 2003ASPC..292..391A). The computer program NAV is introduced, which allows to determine the best fit with 7 "linear" and 5 "non-linear" parameters and their error estimates. The number of parameters is much smaller than for the TP fit (typically 20-40, depending on the width of the eclipse, and is much smaller (5-20) for the W UMa and beta Lyrae - type stars. This causes more smooth approximation taking into account the reflection and ellipsoidal effects (TP2) and generally different shapes of the primary and secondary eclipses. An application of the method to two-color CCD photometry to the recently discovered eclipsing variable 2MASS J18024395 + 4003309 = VSX J180243.9 +400331 (2015JASS...32..101A) allowed to make estimates of the physical parameters of the binary system based on the phenomenological parameters of the light curve. The phenomenological parameters of the light curves were determined for the sample of newly discovered EA and EW - type stars (VSX J223429.3+552903, VSX J223421.4+553013, VSX J223416.2+553424, US-NO-B1.0 1347-0483658, UCAC3-191-085589, VSX J180755.6+074711= UCAC3 196-166827). Despite we have used original observations published by the discoverers, the accuracy estimates of the period using the NAV method are typically better than the original ones.

Time in relativity theory has a status different from that adopted by standard quantum mechanics, where time is considered as a parameter measured with reference to an external absolute Newtonian frame. This status strongly restricts its role in the dynamics of systems and hinders any formulation to merge quantum mechanics with general relativity, specifically when considering quantum gravity. To overcome those limitations, several authors tried to construct an operator which is conjugate to the Hamiltonian of quantum systems implementing some essential features of the relativistic time. These formulations use the concept of internal or intrinsic time instead of the universal coordinate time used in textbooks. Furthermore, recently it is remarked that the consideration of time with relativistic features could enhance the analysis techniques in quantum information processing and have an impact on its status in causal orders and causal structures of quantum information. The role of clocks, their accuracy and stability has become an important issue in quantum information processing. This article present a substantiative review of recent works which reflect the possibility of utilizing quantum time, measured by quantum clock devised according to Page-Wootters scheme, to stand as a resource for quantum information processing.

We propose a modified moment matching algorithm to avoid catastrophic failures for sources with a low signal to noise ratio (SNR). The proposed modifications include a method to eliminate non-physical negative pixel values and a forced single iteration with an initial guess derived from co-add measurements when iterative methods are unstable. We correct for all biases in measurements introduced by the method. We find that the proposed modifications allow the algorithm to avoid catastrophic failures in nearly 100\% of the cases, especially at low signal to noise ratio. Additionally, with a reasonable guess from co-add measurements, the algorithm measures the flux, centroid, size, shape and ellipticity with bias statistically consistent with zero. We show the proposed method allows us to measure sources seven times fainter than traditional methods when applied to images obtained from WIYN-ODI. We also present a scheme to find uncertainties in measurements when using the new method to measure astronomical sources.

We obtain the multiple-parameter quantum Cram\'er-Rao bound for estimating the transverse Cartesian components of the centroid and separation of two incoherent optical point sources using an imaging system with finite spatial bandwidth. Under quite general and realistic assumptions on the point-spread function of the imaging system, and for weak source strengths, we show that the Cram\'er-Rao bounds for the x and y components of the separation are independent of the values of those components, which may be well below the conventional Rayleigh resolution limit. We also propose two linear optics-based measurement methods that approach the quantum bound for the estimation of the Cartesian components of the separation once the centroid has been located. One of the methods is an interferometric scheme that approaches the quantum bound for sub-Rayleigh separations. The other method using fiber coupling can in principle attain the bound regardless of the distance between the two sources.

The wavelength-coverage and sensitivity of JWST now enables us to probe the rest-frame UV - optical spectral energy distributions (SEDs) of galaxies at high-redshift (z>4). From these SEDs it is, in principle, through SED fitting possible to infer key physical properties, including stellar masses, star formation rates, and dust attenuation. These in turn can be compared with the predictions of galaxy formation simulations allowing us to validate and refine the incorporated physics. However, the inference of physical properties, particularly from photometry alone, can lead to large uncertainties and potential biases. Instead, it is now possible, and common, for simulations to be forward-modelled to yield synthetic observations that can be compared directly to real observations. In this work, we measure the JWST broadband fluxes and colours of a robust sample of 5<z<10 galaxies using the Cosmic Evolution Early Release Science (CEERS) Survey. We then analyse predictions from a variety of models using the same methodology and compare the NIRCam/F277W magnitude distribution and NIRCam colours with observations. We find that the predicted and observed magnitude distributions are similar, at least at 5<z<8. At z>8 the distributions differ somewhat, though our observed sample size is small and thus susceptible to statistical fluctuations. Likewise, the predicted and observed colour evolution show broad agreement, at least at 5<z<8. There is however some disagreement between the observed and modelled strength of the strong line contribution. In particular all the models fails to reproduce the F410M-F444W colour at z>8, though, again, the sample size is small here.

We present griz light curves of 251 Type Ia Supernovae (SNe Ia) from the first 3 years of the Dark Energy Survey Supernova Program's (DES-SN) spectroscopically classified sample. The photometric pipeline described in this paper produces the calibrated fluxes and associated uncertainties used in the cosmological parameter analysis (Brout et al. 2018-SYS, DES Collaboration et al. 2018) by employing a scene modeling approach that simultaneously forward models a variable transient flux and temporally constant host galaxy. We inject artificial point sources onto DECam images to test the accuracy of our photometric method. Upon comparison of input and measured artificial supernova fluxes, we find flux biases peak at 3 mmag. We require corrections to our photometric uncertainties as a function of host galaxy surface brightness at the transient location, similar to that seen by the DES Difference Imaging Pipeline used to discover transients. The public release of the light curves can be found at https://des.ncsa.illinois.edu/releases/sn.

Fiber Fabry--Perot (FFP) resonators of a few centimeters are optimized as a function of the reflectivity of the mirrors and the dimensions of the intra-cavity waveguide. Loaded quality factor in excess of 10^9, with an optimum of 4___x___10^9, together with an intrinsic quality factor larger than 10^10 and intrinsic finesse in the range of 10^5 have been measured. An application to the stabilization of laser frequency fluctuations is presented.

Measurements of Higgs boson production cross-sections are carried out in the diphoton decay channel using 139 fb^{-1} of pp collision data at s = 13 TeV collected by the ATLAS experiment at the LHC. The analysis is based on the definition of 101 distinct signal regions using machine-learning techniques. The inclusive Higgs boson signal strength in the diphoton channel is measured to be 1.04^{+0.10}_{-0.09}. Cross-sections for gluon-gluon fusion, vector-boson fusion, associated production with a W or Z boson, and top associated production processes are reported. An upper limit of 10 times the Standard Model prediction is set for the associated production process of a Higgs boson with a single top quark, which has a unique sensitivity to the sign of the top quark Yukawa coupling. Higgs boson production is further characterized through measurements of Simplified Template Cross-Sections (STXS). In total, cross-sections of 28 STXS regions are measured. The measured STXS cross-sections are compatible with their Standard Model predictions, with a p-value of 93%. The measurements are also used to set constraints on Higgs boson coupling strengths, as well as on new interactions beyond the Standard Model in an effective field theory approach. No significant deviations from the Standard Model predictions are observed in these measurements, which provide significant sensitivity improvements compared to the previous ATLAS results.

The decays of pseudoscalar mesons to real and virtual photons as well as neutrino-antineutrino pairs are considered in the framework of the dispersive method based on Anomaly Sum Rules. The contribution of singlet channel involving the new non-perturbative gluon form factor of virtual photon B(q^2) is systematically taken into account. The detailed analysis of its dependence on photon virtuality q^2 relying on the available data for meson transition fomfactors is performed. It is shown that B has quite a nontrivial structure at q^2 sim 1 GeV^2 which may be a signal of the existence of pseudoscalar glueball with a mass about 1.5-2 GeV. The calculation of the decay to νbar ν pairs leads to the compatibility with the result of Arnellos, Marciano and Parsa of 1982, when pion decay is considered neglecting the mixing effects. The account for these effects results, however, in the enhancement of pion branching ratio by a factor of 3, while that for eta decay is larger by several orders of magnitude. It is stressed, that dependence on the pair invariant mass is entirely defined by QCD and coincides with that of the meson transition form factor. The role of obtained results for the physics at HHaS detector at HIAF is discussed.

The seminal Fast Johnson-Lindenstrauss (Fast JL) transform by Ailon and Chazelle (SICOMP'09) embeds a set of n points in d-dimensional Euclidean space into optimal k=O(varepsilon^{-2} ln n) dimensions, while preserving all pairwise distances to within a factor (1 pm varepsilon). The Fast JL transform supports computing the embedding of a data point in O(d ln d +k ln^2 n) time, where the d ln d term comes from multiplication with a d times d Hadamard matrix and the k ln^2 n term comes from multiplication with a sparse k times d matrix. Despite the Fast JL transform being more than a decade old, it is one of the fastest dimensionality reduction techniques for many tradeoffs between varepsilon, d and n. In this work, we give a surprising new analysis of the Fast JL transform, showing that the k ln^2 n term in the embedding time can be improved to (k ln^2 n)/alpha for an alpha = Omega(min{varepsilon^{-1}ln(1/varepsilon), ln n}). The improvement follows by using an even sparser matrix. We also complement our improved analysis with a lower bound showing that our new analysis is in fact tight.

Gravitational lensing is an invaluable probe of the nature of dark matter, and the structures it forms. Lensed gravitational waves in particular allow for unparalleled sensitivity to small scale structures within the lenses, due to the precise time resolution in combination with the continuous monitoring of the entire sky. In this work, we show two distinct ways of using strongly lensed gravitational waves to identify the presence of dark matter subhalos: {i)} through higher order caustics generating high relative magnification (mu_r > 2), short time delay image pairs that break the caustic universality relations of single dark matter halos, which occur for sim 1-10 percent of strongly lensed events in our cold dark matter models, and ii) through the presence of more than three highly magnified images, which occur for sim 0.01-1 percent of the same simulated events. We find that these results are highly sensitive to the concentrations of subhalos in our simulations, and more mildly to their number densities. The presence of low-mass subhalos increases the probability of observing wave-optics lensing in lensed gravitational waves, which is studied by solving the diffraction integral with the stationary phase approximation, as well as numerically. We also report distinct quantitative and qualitative differences in the distributions of relative magnifications and time delays for subhalo populations with increased number densities or concentrations. With the upcoming detection of strongly lensed events by ground- and space- based detectors, comparisons against these simulated distributions will provide insight into the nature of dark matter.

The radial gradient of gas-phase metallicity is a powerful probe of the chemical and structural evolution of star-forming galaxies, closely tied to disk formation and gas kinematics in the early universe. We present spatially resolved chemical and dynamical properties for a sample of 25 galaxies at 0.5 lesssim z lesssim 1.7 from the \msasd survey. These innovative observations provide 3D spectroscopy of galaxies at a spatial resolution approaching JWST's diffraction limit and a high spectral resolution of Rsimeq2700. The metallicity gradients measured in our galaxy sample range from -0.03 to 0.02 dex~kpc^{-1}. Most galaxies exhibit negative or flat radial gradients, indicating lower metallicity in the outskirts or uniform metallicity throughout the entire galaxy. We confirm a tight relationship between stellar mass and metallicity gradient at zsim1 with small intrinsic scatter of 0.02 dex~kpc^{-1}. Our results indicate that metallicity gradients become increasingly negative as stellar mass increases, likely because the more massive galaxies tend to be more ``disky". This relationship is consistent with the predictions from cosmological hydrodynamic zoom-in simulations with strong stellar feedback. This work presents the effort to harness the multiplexing capability of JWST NIRSpec/MSA in slit-stepping mode to map the chemical and kinematic profiles of high-redshift galaxies in large samples and at high spatial and spectral resolution.

Large-Area Picosecond Photo-Detectors (LAPPDs) show great potential for expanding the performance envelope of Micro-Channel Plates (MCPs) to areas of up to 20 x 20 cm and larger. Such scaling introduces new challenges, including how to meet the electronics readout burden of ever larger area MCPs. One solution is to replace the traditional grid anode used for readout with a microwave stripline anode, thus allowing the channel count to scale with MCP width rather than area. However, stripline anodes introduce new issues not commonly dealt with in grid-anodes, especially as their length increases. One of these issues is the near simultaneous arrival of multiple photons on the detector, creating possible confusion about how to reconstruct their arrival times and positions. We propose a maximum a posteriori solution to the problem and verify its performance in simulated scintillator and water-Cherenkov detectors.

This study investigates the morphological classification and drifting rate measurement of the repeating fast radio burst (FRB) source FRB 20240114A using the Five-hundred-meter Aperture Spherical Telescope (FAST). Detected on January 14, 2024, FRB 20240114A exhibited an exceptionally high burst rate, revealing unique properties. Through observational campaigns over several months, we selected a dataset comprising 3,203 bursts (2,109 burst-clusters) during a continuous monitoring session (15,780 seconds) on March 12, 2024. Improving upon previous work, we clarify the definitions of sub-bursts, bursts and burst-clusters. Using an average dispersion measures (DM) of 529.2 pc cm^{-3}, we classified the burst-clusters into Downward Drifting, Upward Drifting, No Drifting, No Evidence for Drifting, Not-Clear, and Complex burst-clusters. Among the 978 burst-clusters that exhibit drifting behavior, 233 (23.82%) show upward drifting. Additionally, if 142 upward drifting single-component burst-clusters are excluded, upward drifting double- and multi-component burst-clusters still account for 10.89% of the 836 burst-clusters exhibiting drifting behavior, equating to 91 burst-clusters. Furthermore, if only upward drifting burst-clusters with consecutive time intervals (or upward drifting bursts) are considered, only 9 bursts remain. Drifting rate comparisons with other physical quantities reveal that the drifting rate increases with peak frequency for single-component burst-clusters with drifting behavior. Moreover, in single-component burst-clusters, those with upward drifting exhibit smaller effective widths, bandwidths, and fluxes than their downward drifting counterparts. A Kolmogorov-Smirnov test further indicates that upward drifting burst-clusters possess longer consecutive time intervals than downward drifting ones, suggesting distinct underlying physical mechanisms.

Two-photon resonance transition technology has been proven to have a wide range of applications,it's limited by the available wavelength of commercial lasers.The application of optical comb technology with direct two-photon transition (DTPT) will not be restricted by cw lasers.This article will further theoretically analyze the dynamics effects of the DTPT process driven by optical frequency combs. In a three-level atomic system, the population of particles and the amount of momentum transfer on atoms are increased compared to that of the DTPT-free process. The 17% of population increasement in 6-level system of cesium atoms has verified that DTPT process has a robust enhancement on the effect of momentum transfer. It can be used to excite the DTPTs of rubidium and cesium simultaneously with the same mode-locked laser. And this technology has potential applications in cooling different atoms to obtain polar cold molecules, as well as high-precision spectroscopy measurement.

In this work we propose an analytical model that reproduces the core-bounds phase of gravitational waves (GW) of Rapidly Rotating (RR) from Core Collapse Supernovae (CCSNe), as a function of three parameters, the arrival time tau, the ratio of the kinetic and potential energy beta and a phenomenological parameter alpha related to rotation and equation of state (EOS). To validate the model we use 126 waveforms from the Richers catalog Richers_2017 selected with the criteria of exploring a range of rotation profiles, and involving EOS. To quantify the degree of accuracy of the proposed model, with a particular focus on the rotation parameter beta, we show that the average Fitting Factor (FF) between the simulated waveforms with the templates is 94.4\%. In order to estimate the parameters we propose a frequentist matched filtering approach in real interferometric noise which does not require assigning any priors. We use the Matched Filter (MF) technique, where we inject a bank of templates considering simulated colored Gaussian noise and the real noise of O3L1. For example for A300w6.00\_BHBLP at 10Kpc we obtain a standar deviation of sigma = 3.34times 10^{-3} for simulated colored Gaussian noise and sigma= 1.46times 10^{-2} for real noise. On the other hand, from the asymptotic expansion of the variance we obtain the theoretical minimum error for beta at 10 kpc and optimal orientation. The estimation error in this case is from 10^{-2} to 10^{-3} as beta increases. We show that the results of the estimation error of beta for the 3-parameter space (3D) is consistent with the single-parameter space (1D), which allows us to conclude that beta is decoupled from the others two parameters.

The peculiar velocities of biased tracers of the cosmic density field contain important information about the growth of large scale structure and generate anisotropy in the observed clustering of galaxies. Using N-body data, we show that velocity expansions for halo redshift-space power spectra are converged at the percent-level at perturbative scales for most line-of-sight angles mu when the first three pairwise velocity moments are included, and that the third moment is well-approximated by a counterterm-like contribution. We compute these pairwise-velocity statistics in Fourier space using both Eulerian and Lagrangian one-loop perturbation theory using a cubic bias scheme and a complete set of counterterms and stochastic contributions. We compare the models and show that our models fit both real-space velocity statistics and redshift-space power spectra for both halos and a mock sample of galaxies at sub-percent level on perturbative scales using consistent sets of parameters, making them appealing choices for the upcoming era of spectroscopic, peculiar-velocity and kSZ surveys.

Future surveys such as the Legacy Survey of Space and Time (LSST) of the Vera C. Rubin Observatory will observe an order of magnitude more astrophysical transient events than any previous survey before. With this deluge of photometric data, it will be impossible for all such events to be classified by humans alone. Recent efforts have sought to leverage machine learning methods to tackle the challenge of astronomical transient classification, with ever improving success. Transformers are a recently developed deep learning architecture, first proposed for natural language processing, that have shown a great deal of recent success. In this work we develop a new transformer architecture, which uses multi-head self attention at its core, for general multi-variate time-series data. Furthermore, the proposed time-series transformer architecture supports the inclusion of an arbitrary number of additional features, while also offering interpretability. We apply the time-series transformer to the task of photometric classification, minimising the reliance of expert domain knowledge for feature selection, while achieving results comparable to state-of-the-art photometric classification methods. We achieve a logarithmic-loss of 0.507 on imbalanced data in a representative setting using data from the Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC). Moreover, we achieve a micro-averaged receiver operating characteristic area under curve of 0.98 and micro-averaged precision-recall area under curve of 0.87.

We report the discovery of a transient seen in a strongly lensed arc at redshift z_{rm s}=1.2567 in Hubble Space Telescope imaging of the Abell 370 galaxy cluster. The transient is detected at 29.51pm0.14 AB mag in a WFC3/UVIS F200LP difference image made using observations from two different epochs, obtained in the framework of the Flashlights program, and is also visible in the F350LP band (m_{rm F350LP} approx 30.53pm0.76 AB mag). The transient is observed on the negative-parity side of the critical curve at a distance of sim 0.6" from it, greater than previous examples of lensed stars. The large distance from the critical curve yields a significantly smaller macromagnification, but our simulations show that bright, O/B-type supergiants can reach sufficiently high magnifications to be seen at the observed position and magnitude. In addition, the observed transient image is a trailing image with an observer-frame time delay of sim+0.8 days from its expected counterpart, so that any transient lasting for longer than that should have also been seen on the minima side and is thus excluded. This, together with the blue colour we measure for the transient (m_{rm F200LP} - m_{rm F350LP} approx [-0.3,-1.6] AB), rules out most other transient candidates such as (kilo)novae, for example, and makes a lensed star the prime candidate. Assuming the transient is indeed a lensed star as suggested, many more such events should be detected in the near future in cluster surveys with the Hubble Space Telescope and James Webb Space Telescope.

Astrophysical uncertainties in dark matter direct detection experiments are typically addressed by parametrizing the velocity distribution in terms of a few uncertain parameters that vary around some central values. Here we propose a method to optimize over all velocity distributions lying within a given distance measure from a central distribution. We discretize the dark matter velocity distribution as a superposition of streams, and use a variety of information divergences to parametrize its uncertainties. With this, we bracket the limits on the dark matter-nucleon and dark matter-electron scattering cross sections, when the true dark matter velocity distribution deviates from the commonly assumed Maxwell-Boltzmann form. The methodology pursued is general and could be applied to other physics scenarios where a given physical observable depends on a function that is uncertain.

We propose the first learning-based approach for fast moving objects detection. Such objects are highly blurred and move over large distances within one video frame. Fast moving objects are associated with a deblurring and matting problem, also called deblatting. We show that the separation of deblatting into consecutive matting and deblurring allows achieving real-time performance, i.e. an order of magnitude speed-up, and thus enabling new classes of application. The proposed method detects fast moving objects as a truncated distance function to the trajectory by learning from synthetic data. For the sharp appearance estimation and accurate trajectory estimation, we propose a matting and fitting network that estimates the blurred appearance without background, followed by an energy minimization based deblurring. The state-of-the-art methods are outperformed in terms of recall, precision, trajectory estimation, and sharp appearance reconstruction. Compared to other methods, such as deblatting, the inference is of several orders of magnitude faster and allows applications such as real-time fast moving object detection and retrieval in large video collections.

While machine-learned models are now routinely employed to facilitate astronomical inquiry, model inputs tend to be limited to a primary data source (namely images or time series) and, in the more advanced approaches, some metadata. Yet with the growing use of wide-field, multiplexed observational resources, individual sources of interest often have a broad range of observational modes available. Here we construct an astronomical multimodal dataset and propose AstroM^3, a self-supervised pre-training approach that enables a model to learn from multiple modalities simultaneously. Specifically, we extend the CLIP (Contrastive Language-Image Pretraining) model to a trimodal setting, allowing the integration of time-series photometry data, spectra, and astrophysical metadata. In a fine-tuning supervised setting, our results demonstrate that CLIP pre-training improves classification performance for time-series photometry, where accuracy increases from 84.6% to 91.5%. Furthermore, CLIP boosts classification accuracy by up to 12.6% when the availability of labeled data is limited, showing the effectiveness of leveraging larger corpora of unlabeled data. In addition to fine-tuned classification, we can use the trained model in other downstream tasks that are not explicitly contemplated during the construction of the self-supervised model. In particular we show the efficacy of using the learned embeddings for misclassifications identification, similarity search, and anomaly detection. One surprising highlight is the "rediscovery" of Mira subtypes and two Rotational variable subclasses using manifold learning and dimension reduction algorithm. To our knowledge this is the first construction of an n>2 mode model in astronomy. Extensions to n>3 modes is naturally anticipated with this approach.

Active Galactic Nuclei (AGN) are some of the most luminous and extreme environments in the Universe. The central engines of AGN, believed to be super-massive black-holes, are fed by accretion discs threaded by magnetic fields within a dense magneto-ionic medium. We report our findings from polarimetric Very-long-baseline Interferometry (VLBI) observations of quasar NRAO150 taken in October 2022 using a combined network of the Very Long Baseline Array (VLBA) and Effelsberg 100-m Radio Telescope. These observations are the first co-temporal multi-frequency polarimetric VLBI observations of NRAO150 at frequencies above 15GHz. We use the new VLBI polarization calibration procedure, GPCAL, with polarization observations of frequencies of 12GHz, 15GHz, 24GHz, and 43GHz of NRAO150. From these observations, we measure Faraday rotation. Using our measurement of Faraday rotation, we also derive the intrinsic electric vector position angle (EVPA0) for the source. As a complementary measurement we determine the behavior of polarization as a function of observed frequency. The polarization from NRAO150 only comes from the core region, with a peak polarization intensity occurring at 24GHz. Across the core region of NRAO150 we see clear gradients in Faraday rotation and EVPA0 values that are aligned with the direction of the jet curving around the core region. We find that for the majority of the polarized region the polarization fraction is greater at higher frequencies, with intrinsic polarization fractions in the core 3%. The Faraday rotation gradients and circular patterns in EVPA0 are strong evidence for a helical/toroidal magnetic field, and the presence of low intrinsic polarization fractions indicate that the polarized emission and hence the helical/toroidal magnetic field, occur within the innermost jet.

We describe pre-perihelion optical observations of interstellar comet 3I/ATLAS taken during July - September 2025 using the Nordic Optical Telescope. Fixed aperture photometry of the comet is well described by a power law function of heliocentric distance, rH, with the exponent (``index") n = 3.8+/-0.3 across the 4.6 au to 1.8 au distance range (phase function 0.04+/-0.02 magnitude/degree assumed). This indicates that the dust production rates vary in proportion to rH**(-1.8+/-0.3). An rH**(-2) variation is expected of a strongly volatile material, and consistent with independent spectroscopic observations showing that carbon dioxide is the primary driver of activity. The measured heliocentric index is unremarkable in the context of solar system comets, for which n is widely dispersed, and provides no basis on which to describe 3I as either dynamically old (thermally processed) or new (pristine). The morphology of the comet changes from a Sun-facing dust fan in the early 2025 July observations, to one dominated by an antisolar dust tail at later dates. We attribute the delayed emergence of the tail to the large size (effective radius 0.1 mm) and slow ejection (5 m/s) of the optically dominant dust particles, and their consequently sluggish response to solar radiation pressure. Small (micron-sized) particles may be present but not in numbers sufficient to dominate the scattering cross-section. Their relative depletion possibly reflects interparticle cohesion, which binds small particles more effectively than large ones. A similar preponderance of 0.1 mm grains was reported in 2I/Borisov. However, 2I differed from 3I in having a much smaller (asteroid-like) heliocentric index, n = 1.9+/-0.1. Dust production rates in 3I are 180 kg/s at 2 au, compared with 70 kg/s in 2I/Borisov at the same distance.

Single-photon LiDAR (SPL) based on single-photon avalanche diode (SPAD) sensing enables time-resolved photon measurements with extreme sensitivity, offering unique potential for active 3D perception in photon-starved scenarios.However, real-world single photon perception remains fundamentally challenging due to unique measurement noise and complex multi-return transient phenomena, which jointly complicate geometric reconstruction and semantic scene understanding. Despite growing interest in SPAD-based sensing, existing studies are largely limited to simulated data or small-scale controlled captures. As a result, systematic evaluation of real-world single photon perception across depth estimation, multi-view reconstruction, and 3D semantic understanding remains underexplored. To bridge this gap, we introduce SP-TransientBench (STB), a real-captured multi-task benchmark for single photon perception. SP-TransientBenc comprises 10 diverse scenes and 10,297 views captured using a solid-state single-photon LiDAR at 256times192 resolution. Each view provides full time-of-flight histograms with multi-return behavior,standardized metadata, and calibrated camera poses for multi-view evaluation. We further provide 13-class 3D semantic annotations for selected scenes. By providing dedicated data splits and evaluation protocols for each task, STB enables consistent and reproducible benchmarking of real-world single photon perception across multiple 3D vision problems. The dataset and code will be released upon acceptance.

JWST has now made it possible to probe the rest-frame optical line emission of high-redshift galaxies extending to z~9, and potentially beyond. To aid in the interpretation of these emerging constraints, in this work we explore predictions for [OIII] emission in high-redshift galaxies using the First Light and Reionisation Epoch Simulations (FLARES). We produce predictions for the [OIII] luminosity function, its correlation with the UV luminosity, and the distribution of equivalent widths (EWs). We also explore how the [OIII] EW correlates with physical properties including specific star formation rate, metallicity, and dust attenuation. Our predictions are largely consistent with recent observational constraints on the luminosity function, average equivalent widths, and line ratios. However, they fail to reproduce the observed tail of high-EW sources and the number density of extreme line emitters. Possibilities to explain these discrepancies include an additional source of ionising photons and/or greater stochasticity in star formation in the model or photometric scatter and/or bias in the observations. With JWST now rapidly building larger samples and a wider range of emission lines the answer to this remaining discrepancy should be available imminently.

We present a new method for measuring the E_G statistic that combines two CMB secondaries -- the kinematic Sunyaev-Zeldovich (kSZ) effect and CMB lensing -- for the first time to probe gravity on linear scales. The E_G statistic is a discriminating tool for modified gravity theories, which leave imprints in lensing observables and peculiar velocities. Existing E_G measurements rely on redshift space distortions (RSD) to infer the velocity field. Here, we employ kSZ velocity-reconstruction instead of RSD, a complementary technique that constrains the largest-scale modes better than the galaxy survey it uses. We construct a novel V_G estimator that involves a ratio between cross-correlations of a galaxy sample with a CMB convergence map and that with a 3D kSZ-reconstructed velocity field. We forecast for current and upcoming CMB maps from the Atacama Cosmology Telescope (ACT) and the Simons Observatory (SO), respectively, in combination with three spectroscopic galaxy samples from the Dark Energy Spectroscopic Instrument (DESI). We find cumulative detection significances in the range S/N sim 20-55, which can robustly test the scale-independent E_G prediction under general relativity (GR) at different effective redshifts of the galaxy samples (zapprox 0.73, 1.33, 1.84). In particular, the SOtimesDESI LRG measurement would be able to distinguish between GR and certain modified gravity models, including Hu-Sawicki f(R) and Chameleon theories, with high confidence. The proposed V_G estimator opens up a new avenue for stress-testing gravity and the LambdaCDM+GR model at the largest observable scales.