Resonance Statistics -Informed Fitting Applied to Automated Cross Section Evaluation
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917444713447424 |
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| author | Fritsch, William Walton, Noah Loring, Justin Forbes, Jacob Zivenko, Oleksii Clark, Aaron Park-Bernstein, Elan Sobes, Vladimir |
| author_facet | Fritsch, William Walton, Noah Loring, Justin Forbes, Jacob Zivenko, Oleksii Clark, Aaron Park-Bernstein, Elan Sobes, Vladimir |
| contents | This work investigates the use of resonance statistics for resonance evaluation to inform spin group assignment and an alternative fitting objective function beyond the commonly used chi-squared statistic. Resonance statistics -informed methods are applied to the automated resonance fitting framework, developed by N. Walton et al. In this automated framework, the utility of resonance statistics is largely unexplored. The new resonance statistics -informed spin group shuffling algorithm reduces spin group frequency bias seen in the base fitting algorithm. Although resonance statistics -informed optimization produces negligible changes in pointwise cross section agreement, it significantly improves consistency with Wigner level-spacing statistics and stabilizes the fitted resonance density in the presence of model imperfections. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_25947 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Resonance Statistics -Informed Fitting Applied to Automated Cross Section Evaluation Fritsch, William Walton, Noah Loring, Justin Forbes, Jacob Zivenko, Oleksii Clark, Aaron Park-Bernstein, Elan Sobes, Vladimir Computational Physics Materials Science Nuclear Theory This work investigates the use of resonance statistics for resonance evaluation to inform spin group assignment and an alternative fitting objective function beyond the commonly used chi-squared statistic. Resonance statistics -informed methods are applied to the automated resonance fitting framework, developed by N. Walton et al. In this automated framework, the utility of resonance statistics is largely unexplored. The new resonance statistics -informed spin group shuffling algorithm reduces spin group frequency bias seen in the base fitting algorithm. Although resonance statistics -informed optimization produces negligible changes in pointwise cross section agreement, it significantly improves consistency with Wigner level-spacing statistics and stabilizes the fitted resonance density in the presence of model imperfections. |
| title | Resonance Statistics -Informed Fitting Applied to Automated Cross Section Evaluation |
| topic | Computational Physics Materials Science Nuclear Theory |
| url | https://arxiv.org/abs/2604.25947 |