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 reactions involving unstable targets are particularly challenging, but often critical for simulations. In response to this need, indirec...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2023-01-01
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| Series: | EPJ Web of Conferences |
| Online Access: | https://www.epj-conferences.org/articles/epjconf/pdf/2023/10/epjconf_nd2023_03012.pdf |
| _version_ | 1797807743738839040 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Escher Jutta |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T06:27:13Z |
| format | Article |
| id | doaj.art-c6c818b021954b3db9d62375797acba8 |
| institution | Directory Open Access Journal |
| issn | 2100-014X |
| language | English |
| last_indexed | 2024-03-13T06:27:13Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | EPJ Web of Conferences |
| spelling | doaj.art-c6c818b021954b3db9d62375797acba82023-06-09T09:16:15ZengEDP SciencesEPJ Web of Conferences2100-014X2023-01-012840301210.1051/epjconf/202328403012epjconf_nd2023_03012Improving nuclear data evaluations with predictive reaction theory and indirect measurementsEscher Jutta0Bergstrom Kirana1Chimanski Emanuel2Gorton Oliver3In Eun Jin4Kruse Michael5Péru Sophie6Pruitt Cole7Rahman Rida8Shinkle Emily9Thapa Aaina10Younes Walid11Lawrence Livermore National LaboratoryUniversity of Colorado DenverBrookhaven National LaboratorySan Diego State UniversityLawrence Livermore National LaboratoryLawrence Livermore National LaboratoryCEA, DAM, DIFLawrence Livermore National LaboratoryUniversity of Tennessee KnoxvilleUniversity of Illinois Urbana-ChampaignLawrence Livermore National LaboratoryLawrence Livermore National LaboratoryNuclear 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.https://www.epj-conferences.org/articles/epjconf/pdf/2023/10/epjconf_nd2023_03012.pdf |
| spellingShingle | 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 Improving nuclear data evaluations with predictive reaction theory and indirect measurements EPJ Web of Conferences |
| title | Improving nuclear data evaluations with predictive reaction theory and indirect measurements |
| title_full | Improving nuclear data evaluations with predictive reaction theory and indirect measurements |
| title_fullStr | Improving nuclear data evaluations with predictive reaction theory and indirect measurements |
| title_full_unstemmed | Improving nuclear data evaluations with predictive reaction theory and indirect measurements |
| title_short | Improving nuclear data evaluations with predictive reaction theory and indirect measurements |
| title_sort | improving nuclear data evaluations with predictive reaction theory and indirect measurements |
| url | https://www.epj-conferences.org/articles/epjconf/pdf/2023/10/epjconf_nd2023_03012.pdf |
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