Improving nuclear data evaluations with predictive reaction theory and indirect measurements

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