Towards accelerated nuclear-physics parameter estimation from binary neutron star mergers: Emulators for the Tolman-Oppenheimer-Volkoff equations
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866909213644554240 |
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| author | Reed, Brendan T. Somasundaram, Rahul De, Soumi Armstrong, Cassandra L. Giuliani, Pablo Capano, Collin Brown, Duncan A. Tews, Ingo |
| author_facet | Reed, Brendan T. Somasundaram, Rahul De, Soumi Armstrong, Cassandra L. Giuliani, Pablo Capano, Collin Brown, Duncan A. Tews, Ingo |
| contents | Gravitational-wave observations of binary neutron-star (BNS) mergers have the potential to revolutionize our understanding of the nuclear equation of state (EOS) and the fundamental interactions that determine its properties. However, Bayesian parameter estimation frameworks do not typically sample over microscopic nuclear-physics parameters that determine the EOS. One of the major hurdles in doing so is the computational cost involved in solving the neutron-star structure equations, known as the Tolman-Oppenheimer-Volkoff (TOV) equations. In this paper, we explore approaches to emulating solutions for the TOV equations: Multilayer Perceptrons (MLP), Gaussian Processes (GP), and a data-driven variant of the reduced basis method (RBM). We implement these emulators for three different parameterizations of the nuclear EOS, each with a different degree of complexity represented by the number of model parameters. We find that our MLP-based emulators are generally more accurate than the other two algorithms whereas the RBM results in the largest speedup with respect to the full, high-fidelity TOV solver. We employ these emulators for a simple parameter inference using a potentially loud BNS observation, and show that the posteriors predicted by our emulators are in excellent agreement with those obtained from the full TOV solver. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_20558 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards accelerated nuclear-physics parameter estimation from binary neutron star mergers: Emulators for the Tolman-Oppenheimer-Volkoff equations Reed, Brendan T. Somasundaram, Rahul De, Soumi Armstrong, Cassandra L. Giuliani, Pablo Capano, Collin Brown, Duncan A. Tews, Ingo High Energy Astrophysical Phenomena General Relativity and Quantum Cosmology Nuclear Theory Gravitational-wave observations of binary neutron-star (BNS) mergers have the potential to revolutionize our understanding of the nuclear equation of state (EOS) and the fundamental interactions that determine its properties. However, Bayesian parameter estimation frameworks do not typically sample over microscopic nuclear-physics parameters that determine the EOS. One of the major hurdles in doing so is the computational cost involved in solving the neutron-star structure equations, known as the Tolman-Oppenheimer-Volkoff (TOV) equations. In this paper, we explore approaches to emulating solutions for the TOV equations: Multilayer Perceptrons (MLP), Gaussian Processes (GP), and a data-driven variant of the reduced basis method (RBM). We implement these emulators for three different parameterizations of the nuclear EOS, each with a different degree of complexity represented by the number of model parameters. We find that our MLP-based emulators are generally more accurate than the other two algorithms whereas the RBM results in the largest speedup with respect to the full, high-fidelity TOV solver. We employ these emulators for a simple parameter inference using a potentially loud BNS observation, and show that the posteriors predicted by our emulators are in excellent agreement with those obtained from the full TOV solver. |
| title | Towards accelerated nuclear-physics parameter estimation from binary neutron star mergers: Emulators for the Tolman-Oppenheimer-Volkoff equations |
| topic | High Energy Astrophysical Phenomena General Relativity and Quantum Cosmology Nuclear Theory |
| url | https://arxiv.org/abs/2405.20558 |