Classifying metal-poor stars with machine learning using nucleosynthesis calculations
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908372380418048 |
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| author | Vassh, Nicole Wang, Yilin Woloshyn, Richard M. Kuchera, Michelle P. Lariviere, Maude Majic, Kayle Cote, Benoit |
| author_facet | Vassh, Nicole Wang, Yilin Woloshyn, Richard M. Kuchera, Michelle P. Lariviere, Maude Majic, Kayle Cote, Benoit |
| contents | We apply the capabilities of machine learning (ML) to discern patterns in order to classify metal-poor stars. To do so, we train an ML model on a bank of nucleosynthesis calculations derived from hydrodynamic simulations for events such as neutron star mergers where the rapid ($r$) neutron capture process can take place. Likewise we consider a bank of calculations from simulations of the slow ($s$) neutron capture process and also consider a few calculations for the intermediate ($i$) neutron capture process. We demonstrate that the ML does well overall in recognizing the $s$ process from the $r$ process, and after training on theoretical calculations ML stellar assignments match conventional labels 87% of the time. We highlight that this method then points to stars that could benefit from additional observational measurements. We also demonstrate that the ML assigns some of the presently considered $i$-process stars to instead be of $r$ or $s$ in origin, but likewise, finds stars currently labeled as $s$ to be potentially more aligned with $i$ enrichment. This first application of ML to classify metal-poor star enrichment using theoretical nucleosynthesis calculations thus reveals the promise, and some challenges, associated with this new data-driven path forward. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_14563 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Classifying metal-poor stars with machine learning using nucleosynthesis calculations Vassh, Nicole Wang, Yilin Woloshyn, Richard M. Kuchera, Michelle P. Lariviere, Maude Majic, Kayle Cote, Benoit Nuclear Theory Astrophysics of Galaxies Solar and Stellar Astrophysics We apply the capabilities of machine learning (ML) to discern patterns in order to classify metal-poor stars. To do so, we train an ML model on a bank of nucleosynthesis calculations derived from hydrodynamic simulations for events such as neutron star mergers where the rapid ($r$) neutron capture process can take place. Likewise we consider a bank of calculations from simulations of the slow ($s$) neutron capture process and also consider a few calculations for the intermediate ($i$) neutron capture process. We demonstrate that the ML does well overall in recognizing the $s$ process from the $r$ process, and after training on theoretical calculations ML stellar assignments match conventional labels 87% of the time. We highlight that this method then points to stars that could benefit from additional observational measurements. We also demonstrate that the ML assigns some of the presently considered $i$-process stars to instead be of $r$ or $s$ in origin, but likewise, finds stars currently labeled as $s$ to be potentially more aligned with $i$ enrichment. This first application of ML to classify metal-poor star enrichment using theoretical nucleosynthesis calculations thus reveals the promise, and some challenges, associated with this new data-driven path forward. |
| title | Classifying metal-poor stars with machine learning using nucleosynthesis calculations |
| topic | Nuclear Theory Astrophysics of Galaxies Solar and Stellar Astrophysics |
| url | https://arxiv.org/abs/2505.14563 |