http://arxiv.org/abs/2209.08883
The equation of state (EoS) that describes extremely dense matter under strong interactions is not completely understood. One reason is that the first-principle calculations of the EoS at finite chemical potential are challenging in nuclear physics. However, neutron star observables like masses, radii, moment of inertia and tidal deformability are direct probes to the EoS and hence make the EoS reconstruction task feasible. In this work, we present results from a novel deep learning technique that optimizes a parameterized equation of state in the automatic differentiation framework. We predict stellar structures from a pre-trained Tolman-Oppenheimer-Volkoff (TOV) solver network, given an EoS represented by neural networks. The latest observational data of neutron stars, specifically their masses and radii, are used to implement the chi-square fitting. We optimize the parameters of the neural network EoS by minimizing the error between observations and predictions. The well-trained neural network EoS gives an estimate of the relationship between the pressure and the mass density. The results presented are consistent with those from conventional approaches and the experimental bound on the tidal deformability inferred from the gravitational wave event, GW170817.
S. Soma, L. Wang, S. Shi, et. al.
Tue, 20 Sep 22
58/81
Comments: 7 pages, 5 figures