2024 | 2023 | 2022 | 2021 | 2020 | 2019 | 2018 | 2017 | 2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008

##### Realizing the potential of deep neural network for analyzing neutron star observables and dense matter equation of state

**Authors**: Thete, A; Banerjee, K; Malik, T

**Ref.**: Phys. Rev. D **108 (6)**, 063028 (2023)

**Abstract**: The difficulty in describing the equation of state (EOS) for nuclear matter at densities above the saturation density (p0) has led to the emergence of a multitude of models based on different assumptions and techniques. These equations of state, 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 stars 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 intercorrelations between the symmetry energy slope, its curvature, and the tidal deformability arising from a set of physical constraints. We also test our network with mock data generated by a different class of physics model, which was not part of the training, to explore the limitations of model dependency in the results. We also study the validity of our trained model using Bayesian inference and show that the performance of our model is on par with physics-based models with the added benefit of much lower computational cost.