EXOVEIL detects single-transit exoplanets via a Transformer world model trained on masked Kepler data, recovering 32% of 1000 ppm injections and 100% of tested TESS planets without retraining.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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astro-ph.EP 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
An ensemble of machine learning models trained jointly on Kepler and TESS data provides instrument-agnostic prioritization of exoplanet candidates.
citing papers explorer
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One Transit Is All You Need: Detecting Exoplanets Through Learned Stellar Behaviour with EXOVEIL
EXOVEIL detects single-transit exoplanets via a Transformer world model trained on masked Kepler data, recovering 32% of 1000 ppm injections and 100% of tested TESS planets without retraining.
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Towards Instrument-Agnostic Exoplanet Candidate Prioritization
An ensemble of machine learning models trained jointly on Kepler and TESS data provides instrument-agnostic prioritization of exoplanet candidates.