Improving nuclear data evaluations with predictive reaction theory and indirect measurements
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Univ. of Colorado, Denver, CO (United States)
- Brookhaven National Laboratory (BNL), Upton, NY (United States)
- San Diego State Univ., CA (United States)
- Alternative Energies and Atomic Energy Commission (CEA/DAM/DIF) (France)
- Univ. of Tennessee, Knoxville, TN (United States)
- Univ. of Illinois at Urbana-Champaign, IL (United States)
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.
- Research Organization:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program
- Grant/Contract Number:
- AC52-07NA27344
- OSTI ID:
- 2204116
- Report Number(s):
- LLNL-JRNL-841573; 1063368; TRN: US2406809
- Journal Information:
- EPJ Web of Conferences (Online), Vol. 284; Conference: 15. International Conference on Nuclear Data for Science and Technology (ND2022), Held Virtually (United States), 21-29 Jul 2022; ISSN 2100-014X
- Publisher:
- EDP SciencesCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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