A flexible Bayesian framework for atomic masses by locally inferring configuration mixing

Abstract Accurate modeling of atomic masses with reliable uncertainty quantification is essential for understanding heavy-element production in astrophysical environments. This remains challenging because uncertainties arise not only from model parameters but also from structural limitations, often leading to underestimation when extrapolating beyond known nuclei. Here, we introduce SPICE, a probabilistic nuclear mass model that uses local Bayesian averaging to emulate mixing between low-lying n