Mapping neutron star data to the equation of state using the deep neural network
The densest state of matter in the Universe is uniquely realized inside the central cores of the neutron star. While first-principles evaluation of the equation of state of such matter remains as one of the long-standing problems in nuclear theory, evaluation in light of neutron star phenomenology i...
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| Veröffentlicht in: | Physical review. D 2020-03, Vol.101 (5), p.1, Article 054016 |
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| container_title | Physical review. D |
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| creator | Fujimoto, Yuki Fukushima, Kenji Murase, Koichi |
| description | The densest state of matter in the Universe is uniquely realized inside the central cores of the neutron star. While first-principles evaluation of the equation of state of such matter remains as one of the long-standing problems in nuclear theory, evaluation in light of neutron star phenomenology is feasible. Here we show results from a novel theoretical technique to utilize a deep neural network with supervised learning. We input up-to-date observational data from neutron star x-ray radiations into the trained neural network and estimate a relation between the pressure and the mass density. Our results are consistent with extrapolation from the conventional nuclear models and the experimental bound on the tidal deformability inferred from gravitational wave observation. |
| doi_str_mv | 10.1103/PhysRevD.101.054016 |
| format | Article |
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While first-principles evaluation of the equation of state of such matter remains as one of the long-standing problems in nuclear theory, evaluation in light of neutron star phenomenology is feasible. Here we show results from a novel theoretical technique to utilize a deep neural network with supervised learning. We input up-to-date observational data from neutron star x-ray radiations into the trained neural network and estimate a relation between the pressure and the mass density. 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D</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fujimoto, Yuki</au><au>Fukushima, Kenji</au><au>Murase, Koichi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Mapping neutron star data to the equation of state using the deep neural network</atitle><jtitle>Physical review. D</jtitle><date>2020-03-01</date><risdate>2020</risdate><volume>101</volume><issue>5</issue><spage>1</spage><pages>1-</pages><artnum>054016</artnum><issn>2470-0010</issn><eissn>2470-0029</eissn><abstract>The densest state of matter in the Universe is uniquely realized inside the central cores of the neutron star. While first-principles evaluation of the equation of state of such matter remains as one of the long-standing problems in nuclear theory, evaluation in light of neutron star phenomenology is feasible. 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| ispartof | Physical review. D, 2020-03, Vol.101 (5), p.1, Article 054016 |
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| source | American Physical Society Journals |
| subjects | Artificial neural networks Equations of state First principles Formability Gravitational waves Machine learning Mapping Neural networks Neutron stars Neutrons Nuclear models Phenomenology |
| title | Mapping neutron star data to the equation of state using the deep neural network |
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