Age Dependence of Occurrence Rate of Close-in Planets around Solar-Type Stars from Hierarchical Bayesian Inference

Estimating how close-in planetary systems evolve requires occurrence-rate measurements that account for both selection effects and uncertainties in host-star properties. I present a hierarchical Bayesian framework that jointly models the stellar population and planet occurrence as functions of stellar and planetary parameters. Instead of adopting point estimates for individual stars, the model propagates the full posterior uncertainties and correlations in stellar age, mass, and metallicity into the population-level inference. This makes it possible to distinguish intrinsic trends in planet occurrence from trends that arise from the host-star sample itself. I first applied this framework to giant planets around Sun-like stars in the California Legacy Survey. The results suggest that the hot Jupiter occurrence declines with stellar age on Gyr timescales, indicating that close-in giant planets may continue to evolve after formation, possibly through tidal or dynamical processes. The broader goal is to place the occurrence of close-in planets on a common statistical footing across stellar populations, and to clarify how age, mass, and metallicity shape planetary-system architectures.