Marín Díaz, Gabriel2025-12-222025-12-222025Marín Díaz, G. (2025). Fas-xai: Fuzzy and explainable ai for interpretable vetting of kepler exoplanet candidates. Mathematics, 13(23), 3796. https://doi.org/10.3390/math132337962227-7390https://hdl.handle.net/11268/16642The detection of exoplanets in space-based photometry relies on identifying periodic transit signatures in stellar light curves. The Kepler Threshold Crossing Events (TCE) catalog collects all periodic dimming signals detected by the pipeline, while the Kepler Objects of Interest (KOI) catalog provides vetted dispositions (CONFIRMED, CANDIDATE, FALSE POSITIVE). However, the pathway from raw TCE detections to KOI classifications remains ambiguous in many borderline cases. We introduce FAS-XAI, a framework that integrates Fuzzy C-Means (FCM) clustering, supervised learning, and explainable AI (XAI) to improve transparency in exoplanet candidate classification. FCM applied to TCE parameters (period, duration, depth, and SNR) reveals three meaningful regimes in the transit-signal space and quantifies ambiguity through fuzzy memberships.engAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/journal article10.3390/math13233796open accessAstrofísicaInteligencia artificialAnálisis estadísticoGoal 9: Build resilient infrastructure, promote sustainable industrialization and foster innovation