Astro-Hunters: Machine Learning for Exoplanet Transit Detection in TESS Photometry

Abstract

Finding planets beyond the Solar System is now largely a data-analysis problem: photometric surveys return far more stellar light curves than can be inspected by eye, and machine learning is increasingly asked to recognise the faint, periodic dimming of a transiting planet. Whether such a detector works turns on a choice that is seldom reported: how its training labels were made. No catalogue disposes the individual measurements a per-cadence detector must classify, so the annotation has to be derived, and this paper asks what that costs. We present Astro-Hunters, an end-to-end pipeline over TESS two-minute photometry: retrieval, detrending, seven sliding-window statistics per cadence, and gradient-boosted classification. These components are deliberately conventional. What is new is the treatment of label provenance as an experimental variable, a corpus of 189,279 cadences from twelve confirmed hosts annotated from published ephemerides rather than from the photometry, and a physical bound on what the task permits. Six classifier families are compared under a star-disjoint protocol. Holding features, model and protocol fixed, precision--recall performance spans a factor of 29 across label sources against 1.8 across architectures. Labels from an isolation forest fitted to the classifier's own features give an apparent AUC of 0.9915 that measures circularity; an unconverted transit epoch drives performance to chance; correct annotation gives AUC 0.788 at 5.3 times the prevalence baseline. The ceiling is observational, not architectural: a median single-cadence signal-to-noise ratio of 2.10 caps per-cadence AUC at 0.932. A Box Least Squares baseline recovers eight of twelve orbital periods from one sector. Phase-folding, not classifier capacity, is what makes the transit signal accessible.