Improving nuclear data evaluations with predictive reaction theory and indirect measurements
Abstrak
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.
Penulis (12)
Jutta Escher
Kirana Bergstrom
Emanuel Chimanski
Oliver Gorton
Eun Jin In
Michael Kruse
Sophie Péru
Cole Pruitt
Rida Rahman
Emily Shinkle
Aaina Thapa
Walid Younes
Akses Cepat
- Tahun Terbit
- 2023
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓