Galactic Chemical Evolution of the Milky Way through a Markov Chain Monte Carlo Approach

The chemical evolution of the Milky Way Galaxy is modeled using a Markov Chain Monte Carlo (MCMC) framework based on an energy minimization criterion to constrain the key parameters governing galactic chemical evolution. The model evolves the Galaxy through successive generations of stars under a multiphase gas infall scenario, where enrichment arises from the nucleosynthetic yields of core-collapse supernovae, binary merger supernovae, and asymptotic giant branch stars. Updated stellar yields from the NuGrid database, together with established massive-star yield sets, are used to provide a consistent and comprehensive treatment of stellar nucleosynthesis across a wide range of stellar masses and metallicities. The simulation predicts the evolution of 83 isotopes of 32 elements (from hydrogen H 1, 2 to galium Ga 69, 71 and lead Pb 206, 207, 208 ) and validates the resulting abundance trends with recent observational surveys of the solar neighborhood. The influence of star formation efficiency, infall timescale, and the delay-time distribution of Type Ia supernovae on the isotopic ratios is systematically examined. The MCMC framework ensures statistically optimal parameter estimation and convergence toward the most probable evolutionary pathway. The resulting models successfully reproduce the observed metallicity distribution and [X/Fe]–[Fe/H] trends within observational uncertainties, establishing a robust probabilistic framework for decoding the chemical enrichment history of the Milky Way.