Toshihiko Kawano

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 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 U 235 and Pu 239 photonuclear reaction cross sections, including photofission ratio data, exposes tensions between current nuclear models and the available experimental data. We present a joint photonuclear evaluation that rigorously incorporates these measurements and reports posterior distributions on the underlying reaction-model parameters. Validation studies show high predictive accuracy (root mean square error < 0.008 barns) and the ability to generalize to unseen nuclei, indicating that the emulator captures meaningful underlying physics relationships. The joint Bayesian inference reveals that the available experimental data cannot be simultaneously reproduced within the current theoretical description. The inferred posterior distributions identify the model parameters and correlated reaction mechanisms most strongly constrained by the data, exposing tensions across isotopes, observables, and reaction channels that remain hidden in traditional localized evaluations. The analysis also revisits a longstanding U 235 photofission cross section discrepancy and reaches conclusions that differ from previous interpretations based on isolated evaluations. More broadly, this work establishes machine-learning-enabled Bayesian inference as a framework for rigorously testing the consistency of photonuclear data and probing the limits of current reaction theory.

A program has been developed to calculate a complete set of γ-ray angular distribution coefficients and statistical tensors in maximally- and partially-aligned nuclei. For practical nuclear structure and reaction purposes, there is no imposed constraint on any arguments that are likely to arise in the determination of these quantities. The program can also be used as a stand-alone vector-coupling calculator for the exact evaluation of Clebsch-Gordan and Racah coefficients, the closely-related Wigner 3-j, 6-j, and 9-j symbols, as well as Gaunt coefficients. These quantities, which frequently arise in quantum mechanical applications involving angular momentum coupling and recoupling schemes, provide the underlying machinery in angular distribution calculations.

1. The neutron-induced reaction cross section is characterized by the ratio of the average resonance width Γ to the average resonance spacing D. In the low-energy region, because the ratio Γ/D is s...