---
tags:
- exoplanet
- transit-detection
- astronomy
- kepler
- tess
- k2
- 1d-cnn
- pytorch
- custom-model
license: apache-2.0
datasets:
- bingbangboom/exoplanet-transit-detection
metrics:
- accuracy
- f1
- precision
- recall
pipeline_tag: tabular-classification
---

# Exoplanet Transit Detector 🔭🪐

A multi-branch 1D CNN for detecting exoplanet transits in stellar light curves,
based on the **AstroNet/ExoMiner++** architecture (NASA Ames).

## Model Architecture

**Multi-Branch 1D CNN** with 5 input branches:
- **Global flux branch**: Phase-folded full light curve (201 bins)
- **Local flux branch**: Zoomed transit view (81 bins)
- **Odd transit branch**: Odd-numbered transits (201 bins)
- **Even transit branch**: Even-numbered transits (201 bins)
- **Scalar features**: Period, duration, depth, stellar parameters (9 features)

Each flux branch uses 2 convolutional blocks with 3 conv layers each (8→16 filters),
batch normalization, and max pooling. Branches are fused and fed through a 4-layer
fully-connected classifier head.

**Total parameters**: 244,181

## Performance

| Metric | Test Set |
|--------|----------|
| **Accuracy** | **89.10%** |
| **F1 (weighted)** | **89.03%** |
| **Precision (weighted)** | **89.03%** |
| **Recall (weighted)** | **89.10%** |
| Loss | 0.2804 |

## Training Details

- **Dataset**: [bingbangboom/exoplanet-transit-detection](https://hf.co/datasets/bingbangboom/exoplanet-transit-detection)
  - Multi-mission: Kepler + TESS + K2
  - 18,853 train / 2,357 val / 2,357 test samples
  - 3-class: PLANET, FALSE_POSITIVE, NO_SIGNAL
- **Optimizer**: AdamW (lr=5e-4, cosine schedule, 5% warmup)
- **Loss**: Weighted cross-entropy (inverse frequency balancing)
- **Epochs**: 30 (best model at epoch 15)
- **Batch size**: 128
- **Architecture reference**: ExoMiner++ (arxiv:2502.09790) & AstroNet-Triage-v2 (arxiv:2301.01371)

## Usage

```python
import torch
import numpy as np
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file

# 1. Download and load model
model_path = hf_hub_download("sarojpatil16/exoplanet-transit-detector", "model.safetensors")

# 2. Recreate architecture (copy from this model card or train.py)
# ... (see model architecture code below) ...
# model = AstroNetCNN(n_scalars=9, num_classes=3)
# model.load_state_dict(load_file(model_path))
# model.eval()

# 3. Prepare inputs from light curve data
# flux_global: (1, 201) - phase-folded full light curve, median-subtracted & MAD-normalized
# flux_local:  (1, 81)  - zoomed transit view
# flux_odd:    (1, 201) - odd-numbered transits
# flux_even:   (1, 201) - even-numbered transits
# scalars:     (1, 9)   - [period_days, duration_hrs, depth_ppm, teff, logg, radius, mass, metallicity, kepmag]
#                          (period, duration, depth are log1p-transformed)

# 4. Predict
# with torch.no_grad():
#     output = model(flux_global, flux_local, flux_odd, flux_even, scalars)
#     probabilities = torch.softmax(output.logits, dim=-1)
#     pred_class = torch.argmax(output.logits, dim=-1)
#     # 0=PLANET, 1=FALSE_POSITIVE, 2=NO_SIGNAL
```

## Label Mapping

| Class ID | Label | Description |
|----------|-------|-------------|
| 0 | PLANET | Confirmed or candidate exoplanet transit |
| 1 | FALSE_POSITIVE | Signal is not a planet (eclipsing binary, stellar variability, etc.) |
| 2 | NO_SIGNAL | No significant transit signal detected |

## References

- Valizadegan et al. (2025), "ExoMiner++: Transit Disposition with Multi-Mission Data and Transfer Learning", [arxiv:2502.09790](https://arxiv.org/abs/2502.09790)
- Yu et al. (2023), "Identifying Exoplanets with Deep Learning. V.", [arxiv:2301.01371](https://arxiv.org/abs/2301.01371)
- Shallue & Vanderburg (2018), "Identifying Exoplanets with Deep Learning", AJ 155:94
- Dataset: [bingbangboom/exoplanet-transit-detection](https://hf.co/datasets/bingbangboom/exoplanet-transit-detection)