http://arxiv.org/abs/2208.13163
The difficulty in describing the equation of state (EoS) for nuclear matter at densities above the saturation density ($\rho_0$) has led to the emergence of a multitude of models based on different assumptions and techniques. These EoSs, when used to describe a neutron star (NS), lead to differing values of observables. An outstanding goal in astrophysics is to constrain the dense matter EoS by exploiting astrophysical and gravitational wave measurements. Nuclear matter parameters appear as Taylor coefficients in the expansion of the EoS around the saturation density of symmetric and asymmetric nuclear matter, and provide a physically-motivated representation of the EoS. In this paper, we introduce a deep learning-based methodology to predict key neutron star observables such as the NS mass, NS radius, and tidal deformability from a set of nuclear matter parameters. Using generated mock data, we confirm that the neural network model is able to accurately capture the underlying physics of finite nuclei and replicate inter-correlations between the symmetry energy slope, its curvature and the tidal deformability arising from a set of physical constraints. We also perform a systematic Bayesian estimation of NMPs in light of recent observational data with the trained neural network and study the effects of correlations among these NMPs. We show that by not considering inter-correlations arising from finite nuclei constraints, an intrinsic uncertainty of upto 30% can be observed on higher-order NMPs.
A. Thete, K. Banerjee and T. Malik
Tue, 30 Aug 22
25/76
Comments: 23 pages, 5 figures, 7 tables