{"ID":3006033,"CreatedAt":"2026-06-03T03:09:48.883664427Z","UpdatedAt":"2026-06-04T18:25:52.90293501Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.02778","arxiv_id":"2606.02778","title":"One Transit Is All You Need: Detecting Exoplanets Through Learned Stellar Behaviour with EXOVEIL","abstract":"I present EXOVEIL, a transit detection system that learns what a star's brightness should look like and flags when reality disagrees. Unlike existing systems that require phase-folded input, EXOVEIL operates on raw flux time series and can detect planets that transit only once.A Transformer world model, trained on 16,499 Kepler light curves with transit-masked self-supervised learning, predicts expected stellar flux. A matched-filter detector with variance weighting extracts transit signals from the prediction residuals. A learned classifier (XGBoost) separates planets from false positives, achieving AUC 0.938 on Kepler DR25. Applied to single-transit injection-recovery, EXOVEIL recovers 32% of transits at 1000 ppm depth a task where all classification-based systems score 0% by construction. A blind search of 3,737 Kepler stars yields 179 new transit-like signals not present in the DR25 TCE catalogue, including 46 monotransit candidates. Applied withoutretraining to 47 confirmed TESS planets in the PLATO LOPS2 field, EXOVEIL achieves 100% recovery, demonstrating zero-shot cross-mission transfer. At PLATO's 25-second cadence, detection reaches 100 ppm -- approaching the Earth-analog regime. I provide the first application of conformal prediction to transit detection (95.9% empirical coverage) and release the system as pip install exoveil with pretrained weights and a candidate catalogue.","short_abstract":"I present EXOVEIL, a transit detection system that learns what a star's brightness should look like and flags when reality disagrees. Unlike existing systems that require phase-folded input, EXOVEIL operates on raw flux time series and can detect planets that transit only once.A Transformer world model, trained on 16,4...","url_abs":"https://arxiv.org/abs/2606.02778","url_pdf":"https://arxiv.org/pdf/2606.02778v1","authors":"[\"Pratik Priyanshu\"]","published":"2026-06-01T18:41:10Z","proceeding":"astro-ph.EP","tasks":"[\"astro-ph.EP\",\"astro-ph.IM\",\"cs.LG\"]","methods":"[\"Transformer\"]","has_code":false}