Deep-learning emulator for photonuclear reactions and a framework for Bayesian joint evaluations via Hamiltonian Monte Carlo

Photonuclear data libraries provide a quantitative description of photon interaction physics for nuclear reaction modeling and underpin a broad range of applications. However, existing evaluations remain limited by persistent inconsistencies between experimental measurements and theoretical descriptions, particularly when multiple isotopes and reaction channels must be treated simultaneously. Correlations induced by shared reaction-model parameters across isotopes, observables, and reaction channels are difficult to rigorously propagate within traditional localized evaluation approaches, motivating joint inference methodologies for physically consistent photonuclear evaluation. In this article, we present a deep-neural network emulator of ${\mathrm{COH}}_{3}$ reaction modeling code that accurately reproduces theoretical calculations with substantial speedup and enables a Markov Chain Monte Carlo inference. We show that a statistically consistent joint Bayesian treatment of $^{235}\math