We investigate the feasibility of detecting exomoon candidates using the Earth 2.0 (ET) mission, a space-based telescope designed for detecting Earth-like planets using high-precision photometry. We employed a photodynamical simulation framework to model both three-body star–planet–moon systems and two-body star–planet systems, generating synthetic light curves for a variety of exomoon configurations under the assumption of idealized white-noise-dominated observations. These light curves were analyzed using the package to extract key transit parameters, including mid-transit times, transit depths, and transit durations. We then assessed exomoon detectability by comparing the metrics from three-body systems with two-body models, focusing on transit timing variations (TTVs). When the TTVs from the two models are statistically distinguishable, we are able to conclude that the exomoon signal is detectable. Our results show that, in this idealized noise regime, while the detection probabilities for Galilean-like exomoons are very low, larger exomoons with short orbital periods around gas giants exhibit significantly higher detection probabilities. In particular, our simulations demonstrate that ET could detect exomoon candidates similar to the well-known exomoon candidate around Kepler-1625 b. While we also investigate the use of transit duration variations (TDVs), transit radius variations (TRVs), flat-bottomed transit duration variations (TFVs), and impact parameter variations (TbVs), TTVs remain the most effective method. These findings highlight the potential of the ET mission to detect exomoon candidates, with its high photometric precision enabling the identification of subtle dynamical signatures induced by the existence of exomoons orbiting exoplanets. We emphasize, however, that these results represent the theoretical best-case performance, as stellar variability, instrumental systematics, and other unknown noise sources are not included in this analysis. The simulated ET exomoon light curve dataset will also be made publicly available to the community. PyTransit
Cui et al. (Tue,) studied this question.