TY  - JOUR
A1  - Soma, Shriya
A1  - Wang, Lingxiao
A1  - Shi, Shuzhe
A1  - Stöcker, Horst
A1  - Zhou, Kai
T1  - A physics-based neural network reconstruction of the dense matter equation of state from neutron star observables
T2  - The European physical journal. Web of Conferences
N2  - 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.
Y1  - 2023
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/82566
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-825665
SN  - 2100-014X
VL  - 276
IS  - 06007
PB  - EDP Sciences
CY  - Les Ulis
ER  -