Improving nuclear data evaluations with predictive reaction theory and indirect measurements Article Swipe
Jutta Escher
,
Kirana Bergstrom
,
Emanuel Chimanski
,
Oliver C. Gorton
,
Eun Jin In
,
Michael Kruse
,
S. Péru
,
C. D. Pruitt
,
Rida Rahman
,
Emily Shinkle
,
Aaina Bansal
,
W. Younes
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2304.10034
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2304.10034
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.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2304.10034
- https://arxiv.org/pdf/2304.10034
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4366733003
All OpenAlex metadata
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- DOI
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https://doi.org/10.48550/arxiv.2304.10034Digital Object Identifier
- Title
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Improving nuclear data evaluations with predictive reaction theory and indirect measurementsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-04-20Full publication date if available
- Authors
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Jutta Escher, Kirana Bergstrom, Emanuel Chimanski, Oliver C. Gorton, Eun Jin In, Michael Kruse, S. Péru, C. D. Pruitt, Rida Rahman, Emily Shinkle, Aaina Bansal, W. YounesList of authors in order
- Landing page
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https://arxiv.org/abs/2304.10034Publisher landing page
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https://arxiv.org/pdf/2304.10034Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2304.10034Direct OA link when available
- Concepts
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Nuclear reaction, Nuclear data, Nuclear physics, Inference, Nuclear astrophysics, Computer science, Experimental data, Neutron, Statistical physics, Physics, Biological system, Artificial intelligence, Mathematics, Statistics, BiologyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.reactions | 14, 22 |
| abstract_inverted_index.surrogate | 44, 74 |
| abstract_inverted_index.developed. | 49 |
| abstract_inverted_index.inference. | 94 |
| abstract_inverted_index.predicted. | 20 |
| abstract_inverted_index.approaches, | 40 |
| abstract_inverted_index.incomplete, | 9 |
| abstract_inverted_index.microscopic | 80 |
| abstract_inverted_index.predictions | 81 |
| abstract_inverted_index.scattering, | 85 |
| abstract_inverted_index.theoretical | 70 |
| abstract_inverted_index.applications | 7 |
| abstract_inverted_index.astrophysics | 5 |
| abstract_inverted_index.capabilities | 71 |
| abstract_inverted_index.challenging, | 28 |
| abstract_inverted_index.particularly | 27 |
| abstract_inverted_index.simulations. | 33 |
| abstract_inverted_index.measurements. | 62, 76 |
| abstract_inverted_index.neural-network | 91 |
| abstract_inverted_index.Neutron-induced | 21 |
| abstract_inverted_index.nucleon-nucleus | 88 |
| abstract_inverted_index.charged-particle | 83 |
| abstract_inverted_index.uncertainty-quantified | 86 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 12 |
| citation_normalized_percentile.value | 0.0578609 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |