Probing nuclear physics with supernova gravitational waves and machine learning
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866929304471863296 |
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| author | Mitra, Ayan Orel, Daniil Abylkairov, Y. Sultan Shukirgaliyev, Bekdaulet Abdikamalov, Ernazar |
| author_facet | Mitra, Ayan Orel, Daniil Abylkairov, Y. Sultan Shukirgaliyev, Bekdaulet Abdikamalov, Ernazar |
| contents | Core-collapse supernovae are sources of powerful gravitational waves (GWs). We assess the possibility of extracting information about the equation of state (EOS) of high density matter from the GW signal. We use the bounce and early post-bounce signals of rapidly rotating supernovae. A large set of GW signals is generated using general relativistic hydrodynamics simulations for various EOS models. The uncertainty in the electron capture rate is parametrized by generating signals for six different models. To classify EOSs based on the GW data, we train a convolutional neural network (CNN) model. Even with the uncertainty in the electron capture rates, we find that the CNN models can classify the EOSs with an average accuracy of about 87 percent for a set of four distinct EOS models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_15649 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Probing nuclear physics with supernova gravitational waves and machine learning Mitra, Ayan Orel, Daniil Abylkairov, Y. Sultan Shukirgaliyev, Bekdaulet Abdikamalov, Ernazar High Energy Astrophysical Phenomena Core-collapse supernovae are sources of powerful gravitational waves (GWs). We assess the possibility of extracting information about the equation of state (EOS) of high density matter from the GW signal. We use the bounce and early post-bounce signals of rapidly rotating supernovae. A large set of GW signals is generated using general relativistic hydrodynamics simulations for various EOS models. The uncertainty in the electron capture rate is parametrized by generating signals for six different models. To classify EOSs based on the GW data, we train a convolutional neural network (CNN) model. Even with the uncertainty in the electron capture rates, we find that the CNN models can classify the EOSs with an average accuracy of about 87 percent for a set of four distinct EOS models. |
| title | Probing nuclear physics with supernova gravitational waves and machine learning |
| topic | High Energy Astrophysical Phenomena |
| url | https://arxiv.org/abs/2310.15649 |