FAS-XAI: Fuzzy and Explainable AI for Interpretable Vetting of Kepler Exoplanet Candidates
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 provi...
| Autor: | |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universidad Europea (UEM) |
| Repositorio: | ABACUS. Repositorio de Producción Científica |
| Idioma: | inglés |
| OAI Identifier: | oai:abacus.universidadeuropea.com:11268/16642 |
| Acceso en línea: | https://hdl.handle.net/11268/16642 |
| Access Level: | acceso abierto |
| Palabra clave: | Astrofísica Inteligencia artificial Análisis estadístico Goal 9: Build resilient infrastructure, promote sustainable industrialization and foster innovation |
| Sumario: | 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. |
|---|