Interpretability as a leakage detector: a case study in machine-learning-based exoplanet detection with TESS

Abstract

The Transiting Exoplanet Survey Satellite (TESS) generates photometric time series for hundreds of thousands of stars, far more than can be vetted by hand, motivating machine-learning (ML) triage. We present a low-resource, interpretable pipeline that classifies TESS light curves as transit-bearing or not using a random forest trained on engineered summary features, treating feature-importance analysis as a formal auditing step, and apply it to Sector 68. The positive class comprises TESS-confirmed planet hosts; the negative class comprises Sector 68 targets with no catalogued planet or candidate, assembled from a different source.