A physics-based neural network reconstruction of the dense matter equation of state from neutron star observables
- We introduce a novel technique that utilizes a physics-driven deep learning method to reconstruct the dense matter equation of state from neutron star observables, particularly the masses and radii. The proposed framework involves two neural networks: one to optimize the EoS using Automatic Differentiation in the unsupervised learning scheme; and a pre-trained network to solve the Tolman–Oppenheimer–Volkoff (TOV) equations. The gradient-based optimization process incorporates a Bayesian picture into the proposed framework. The reconstructed EoS is proven to be consistent with the results from conventional methods. Furthermore, the resulting tidal deformation is in agreement with the limits obtained from the gravitational wave event, GW170817.
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| Author: | Shriya SomaORCiDGND, Lingxiao WangORCiD, Shuzhe ShiORCiDGND, Horst StöckerORCiDGND, Kai ZhouORCiD |
|---|---|
| URN: | urn:nbn:de:hebis:30:3-825665 |
| DOI: | https://doi.org/10.1051/epjconf/202327606007 |
| ISSN: | 2100-014X |
| Parent Title (English): | The European physical journal. Web of Conferences |
| Publisher: | EDP Sciences |
| Place of publication: | Les Ulis |
| Document Type: | Article |
| Language: | English |
| Date of Publication (online): | 2023/03/01 |
| Date of first Publication: | 2023/03/01 |
| Publishing Institution: | Universitätsbibliothek Johann Christian Senckenberg |
| Contributing Corporation: | International Conference on Strangeness in Quark Matter (20. : 2022 : Busan) |
| Release Date: | 2024/05/08 |
| Volume: | 276 |
| Issue: | 06007 |
| Article Number: | 06007 |
| Page Number: | 4 |
| Institutes: | Physik / Physik |
| Wissenschaftliche Zentren und koordinierte Programme / Frankfurt Institute for Advanced Studies (FIAS) | |
| Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
| 5 Naturwissenschaften und Mathematik / 53 Physik / 530 Physik | |
| Sammlungen: | Universitätspublikationen |
| Licence (German): |  Creative Commons - CC BY - Namensnennung 4.0 International |
