Rigel (United States)

A Machine Learning Framework for Identifying and Detecting Potentially Habitable Exomoons Around Sub-Neptunian Planets

This study presents a conceptual two-stage machine learning framework for identifying and detecting potentially habitable exomoons around sub-Neptunian planets. The framework addresses the difficulty of detecting weak, indirect, and phase-varying exomoon signatures by combining p...

A Machine Learning Framework for Identifying and Detecting Potentially Habitable Exomoons Around Sub-Neptunian Planets

This study presents a conceptual two-stage machine learning framework for identifying and detecting potentially habitable exomoons around sub-Neptunian planets. The framework addresses the difficulty of detecting weak, indirect, and phase-varying exomoon signatures by combining p...