New approach for the quantification of uncertainties in reaction modeling via data-driven multi-objective optimization
Fuente:
arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913933192855552 |
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| author | Dimitrakopoulos, N. Perdikakis, G. Montes, F. Gastis, P. Kuvin, S. A. Lee, H. Y. Tsintari, P. Voinov, A. V. |
| author_facet | Dimitrakopoulos, N. Perdikakis, G. Montes, F. Gastis, P. Kuvin, S. A. Lee, H. Y. Tsintari, P. Voinov, A. V. |
| contents | We introduce a new multi-objective optimization approach to determine uncertainty-quantified nuclear reaction parameters in the Hauser-Feshbach framework. By simultaneously accounting for all available data across multiple reaction channels we capture parameter correlations and estimate data-driven uncertainties. We implement in the Ni-Ge region yielding uncertainty-quantified model parameters for both stable and unstable isotopes. We estimate resonance spacings for nuclei beyond experimental reach and validate our method by calculating a known cross-section outside our optimization region. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_06370 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | New approach for the quantification of uncertainties in reaction modeling via data-driven multi-objective optimization Dimitrakopoulos, N. Perdikakis, G. Montes, F. Gastis, P. Kuvin, S. A. Lee, H. Y. Tsintari, P. Voinov, A. V. Nuclear Theory We introduce a new multi-objective optimization approach to determine uncertainty-quantified nuclear reaction parameters in the Hauser-Feshbach framework. By simultaneously accounting for all available data across multiple reaction channels we capture parameter correlations and estimate data-driven uncertainties. We implement in the Ni-Ge region yielding uncertainty-quantified model parameters for both stable and unstable isotopes. We estimate resonance spacings for nuclei beyond experimental reach and validate our method by calculating a known cross-section outside our optimization region. |
| title | New approach for the quantification of uncertainties in reaction modeling via data-driven multi-objective optimization |
| topic | Nuclear Theory |
| url | https://arxiv.org/abs/2507.06370 |