Toward More Realistic Machine-Learning Inference of the Dense-Matter Equation of State from Supernova Gravitational Waves
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866918415261761536 |
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| author | Akhmetali, Almat Abylkairov, Y. Sultan Zaidyn, Marat Sakan, Aknur Zhunuskanov, Alisher Ussipov, Nurzhan Font, José Antonio Torres-Forné, Alejandro Abdikamalov, Ernazar |
| author_facet | Akhmetali, Almat Abylkairov, Y. Sultan Zaidyn, Marat Sakan, Aknur Zhunuskanov, Alisher Ussipov, Nurzhan Font, José Antonio Torres-Forné, Alejandro Abdikamalov, Ernazar |
| contents | Gravitational waves from core-collapse supernovae offer a unique probe of the equation of state (EOS) of dense nuclear matter. For rapidly rotating stars, previous machine-learning studies demonstrated promising EOS classification accuracy. However, these analyses relied on several simplifying assumptions. In this work, we relax three key assumptions. First, we include real detector noise. Second, we expand the analysis from a single progenitor model to four models spanning 12 to 40 solar masses, and for each mass we consider multiple rotational configurations, from slow to rapid. Third, we introduce uncertainty in the core bounce time of up to 20 ms, rather than assuming it is known precisely. We find that none of these effects significantly degrades EOS classification performance. Instead, the larger dataset associated with multiple progenitor models and noise realizations improves training and classification accuracy. This study is a step in a broader effort to progressively incorporate more realistic conditions into gravitational-wave inference for core-collapse supernovae. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_27680 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Toward More Realistic Machine-Learning Inference of the Dense-Matter Equation of State from Supernova Gravitational Waves Akhmetali, Almat Abylkairov, Y. Sultan Zaidyn, Marat Sakan, Aknur Zhunuskanov, Alisher Ussipov, Nurzhan Font, José Antonio Torres-Forné, Alejandro Abdikamalov, Ernazar High Energy Astrophysical Phenomena Gravitational waves from core-collapse supernovae offer a unique probe of the equation of state (EOS) of dense nuclear matter. For rapidly rotating stars, previous machine-learning studies demonstrated promising EOS classification accuracy. However, these analyses relied on several simplifying assumptions. In this work, we relax three key assumptions. First, we include real detector noise. Second, we expand the analysis from a single progenitor model to four models spanning 12 to 40 solar masses, and for each mass we consider multiple rotational configurations, from slow to rapid. Third, we introduce uncertainty in the core bounce time of up to 20 ms, rather than assuming it is known precisely. We find that none of these effects significantly degrades EOS classification performance. Instead, the larger dataset associated with multiple progenitor models and noise realizations improves training and classification accuracy. This study is a step in a broader effort to progressively incorporate more realistic conditions into gravitational-wave inference for core-collapse supernovae. |
| title | Toward More Realistic Machine-Learning Inference of the Dense-Matter Equation of State from Supernova Gravitational Waves |
| topic | High Energy Astrophysical Phenomena |
| url | https://arxiv.org/abs/2603.27680 |