EPJ Web of Conferences 284, 03012 (2023)
https://doi.org/10.1051/epjconf/202328403012

Improving nuclear data evaluations with predictive reaction theory and indirect measurements

¹ Lawrence Livermore National Laboratory, Livermore, CA 94551, USA
² University of Colorado Denver, Denver, CO 80204, USA
³ Brookhaven National Laboratory, Upton, NY 11973, USA
⁴ San Diego State University, San Diego, 92182, USA
⁵ CEA, DAM, DIF, F-91680 Arpajon, France
⁶ University of Tennessee Knoxville, Knoxville, TN 37996, USA
⁷ University of Illinois Urbana-Champaign, IL 61801, USA

^* Corresponding author: escher1@llnl.gov

Published online: 26 May 2023

Abstract

Nuclear reaction data required for astrophysics and applications is incomplete, as not all nuclear reactions can be measured or reliably predicted. Neutron-induced reactions involving unstable targets are particularly challenging, but often critical for simulations. In response to this need, indirect approaches, such as the surrogate reaction method, have been developed. Nuclear theory is key to extract reliable cross sections from such indirect measurements. We describe ongoing efforts to expand the theoretical capabilities that enable surrogate reaction measurements. We focus on microscopic predictions for charged-particle inelastic scattering, uncertainty-quantified optical nucleon-nucleus models, and neural-network enhanced parameter inference.