exoveil 0.2.1

Detecting single-transit exoplanets through learned stellar behaviour

EXOVEIL

Detecting single-transit exoplanets through learned stellar behaviour.

EXOVEIL is a world-model-based transit detection system. It learns what a star's brightness should look like and flags when reality disagrees. Unlike ExoMiner and AstroNet, it detects planets that transit only once.

Quick Start

from exoveil import ExoVeil

model = ExoVeil.from_pretrained()
results = model.detect("KIC 11812062")

for event in results['events']:
    print(f"Transit at t={event['time']:.2f}, SNR={event['snr']:.1f}, depth={event['depth_ppm']:.0f} ppm")

Key Results

• Single-transit detection: 32% recovery at 1000 ppm (ExoMiner/AstroNet: 0%)
• 179 new candidate signals in Kepler data not in DR25 TCE catalog
• 47/47 TESS planets recovered in PLATO LOPS2 field (zero-shot transfer)
• 100% recovery at PLATO cadence down to 100 ppm
• Conformal uncertainty guarantees: 95.9% coverage
• Classification AUC: 0.938 on Kepler DR25

Works With

• Kepler light curves (model.detect("KIC 11812062"))
• TESS light curves (model.detect("TIC 25155310"))
• Raw flux arrays (model.detect_from_array(time, flux))
• Any photometric time series

Citation

@article{priyanshu2026exoveil,
    title={EXOVEIL: Detecting Single-Transit Exoplanets Through Learned Stellar Behaviour},
    author={Priyanshu, Pratik},
    journal={arXiv preprint arXiv:XXXX.XXXXX},
    year={2026}
}