FAS-XAI: fuzzy and explainable AI for interpretable vetting of Kepler exoplanet candidates

Abstract

The 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. Linking these clusters to KOI dispositions highlights a progressive consolidation of confirmed planets within the high-SNR, medium-duration regime. A supervised XGBoost classifier trained on KOI labels and augmented with fuzzy memberships achieves strong performance (Accuracy = 0.73, Macro F1 = 0.69, ROC–AUC = 0.855), clearly separating CONFIRMED and FALSE POSITIVE objects while appropriately reflecting the transitional nature of CANDIDATES. SHAP, LIME, and ELI5 provide consistent global and local attributions, identifying period, duration, depth, SNR, and fuzzy ambiguity as the key explanatory features. Finally, stellar parameters from Kepler DR25 validate the physical plausibility of the detected regimes, demonstrating that FAS-XAI captures astrophysically meaningful patterns rather than purely statistical structures. Overall, the framework illustrates how fuzzy logic and explainable AI can jointly enhance the interpretability and scientific rigor of exoplanet vetting pipelines.