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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.05871 |
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| _version_ | 1866910604975931392 |
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| author | Peng, Yinglei Ristić, Marko Kedia, Atul O'Shaughnessy, Richard Fontes, Christopher J. Fryer, Chris L. Korobkin, Oleg Mumpower, Matthew R. Villar, V. Ashley Wollaeger, Ryan T. |
| author_facet | Peng, Yinglei Ristić, Marko Kedia, Atul O'Shaughnessy, Richard Fontes, Christopher J. Fryer, Chris L. Korobkin, Oleg Mumpower, Matthew R. Villar, V. Ashley Wollaeger, Ryan T. |
| contents | Kilonovae are the electromagnetic transients created by the radioactive decay of freshly synthesized elements in the environment surrounding a neutron star merger. To study the fundamental physics in these complex environments, kilonova modeling requires, in part, the use of radiative transfer simulations. The microphysics involved in these simulations results in high computational cost, prompting the use of emulators for parameter inference applications. Utilizing a training set of 22248 high-fidelity simulations (composed of 412 unique ejecta parameter combinations evaluated at 54 viewing angles), we use a neural network to efficiently train on existing radiative transfer simulations and predict light curves for new parameters in a fast and computationally efficient manner. Our neural network can generate millions of new light curves in under a minute. We discuss our emulator's degree of off-sample reliability and parameter inference of the AT2017gfo observational data. Finally, we discuss tension introduced by multi-band inference in the parameter inference results, particularly with regard to the neural network's recovery of viewing angle. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_05871 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Kilonova Light-Curve Interpolation with Neural Networks Peng, Yinglei Ristić, Marko Kedia, Atul O'Shaughnessy, Richard Fontes, Christopher J. Fryer, Chris L. Korobkin, Oleg Mumpower, Matthew R. Villar, V. Ashley Wollaeger, Ryan T. High Energy Astrophysical Phenomena Kilonovae are the electromagnetic transients created by the radioactive decay of freshly synthesized elements in the environment surrounding a neutron star merger. To study the fundamental physics in these complex environments, kilonova modeling requires, in part, the use of radiative transfer simulations. The microphysics involved in these simulations results in high computational cost, prompting the use of emulators for parameter inference applications. Utilizing a training set of 22248 high-fidelity simulations (composed of 412 unique ejecta parameter combinations evaluated at 54 viewing angles), we use a neural network to efficiently train on existing radiative transfer simulations and predict light curves for new parameters in a fast and computationally efficient manner. Our neural network can generate millions of new light curves in under a minute. We discuss our emulator's degree of off-sample reliability and parameter inference of the AT2017gfo observational data. Finally, we discuss tension introduced by multi-band inference in the parameter inference results, particularly with regard to the neural network's recovery of viewing angle. |
| title | Kilonova Light-Curve Interpolation with Neural Networks |
| topic | High Energy Astrophysical Phenomena |
| url | https://arxiv.org/abs/2402.05871 |