Constraining Nuclear Mass Models Using r-process Observables with Multi-objective Optimization
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866916782741127168 |
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| author | Li, Mengke Mumpower, Matthew Vassh, Nicole Porter, William Samuel Surman, Rebecca |
| author_facet | Li, Mengke Mumpower, Matthew Vassh, Nicole Porter, William Samuel Surman, Rebecca |
| contents | Predicting nuclear masses is a longstanding challenge. One path forward is machine learning (ML) which trains on experimental data, but can suffer large errors when extrapolating toward neutron-rich species. In nature, such masses shape observables for the rapid neutron capture process (r-process), which in principle could inform ML models. Here we introduce a multi-objective optimization approach using the Pareto Front algorithm. We show that this technique, capable of identifying models which generate r-process abundances aligning with both Solar and stellar data, is a promising method to select ML models with reliable extrapolation power. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_06464 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Constraining Nuclear Mass Models Using r-process Observables with Multi-objective Optimization Li, Mengke Mumpower, Matthew Vassh, Nicole Porter, William Samuel Surman, Rebecca Solar and Stellar Astrophysics Predicting nuclear masses is a longstanding challenge. One path forward is machine learning (ML) which trains on experimental data, but can suffer large errors when extrapolating toward neutron-rich species. In nature, such masses shape observables for the rapid neutron capture process (r-process), which in principle could inform ML models. Here we introduce a multi-objective optimization approach using the Pareto Front algorithm. We show that this technique, capable of identifying models which generate r-process abundances aligning with both Solar and stellar data, is a promising method to select ML models with reliable extrapolation power. |
| title | Constraining Nuclear Mass Models Using r-process Observables with Multi-objective Optimization |
| topic | Solar and Stellar Astrophysics |
| url | https://arxiv.org/abs/2506.06464 |