| Autor: |
Escher Jutta, Bergstrom Kirana, Chimanski Emanuel, Gorton Oliver, In Eun Jin, Kruse Michael, Péru Sophie, Pruitt Cole, Rahman Rida, Shinkle Emily, Thapa Aaina, Younes Walid |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
EPJ Web of Conferences, Vol 284, p 03012 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2100-014X |
| DOI: |
10.1051/epjconf/202328403012 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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