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

Abstract

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 population-level target prioritization with sequence-aware analysis of heterogeneous observational data. Stage 1 uses planetary, stellar, orbital, and derived dynamical properties to construct a physics-informed opportunity map for ranking planetary systems according to their potential to host stable, detectable, and potentially habitable moons. Stage 2 applies machine learning and deep learning methods to time-dependent observational channels, including transit timing, transit duration, transit morphology, and complementary measurements, with emphasis on multichannel evidence integration, physical consistency, and calibrated uncertainty. The study focuses on sub-Neptunian planets (1.7–4.0 Earth radii), because they may provide a useful balance between dynamical suitability, potential moon habitability, observational detectability, and population abundance. Synthetic data, simulation-based inference, injection-recovery experiments, and closed-loop model refinement are proposed as the primary methods for future validation. The framework is intended to support target prioritization, observational planning, model development, and future empirical exomoon searches as higher-quality photometric, radial-velocity, astrometric, direct-imaging, and related datasets become available.