Speaker
Description
Sagnik Chatterjee, IISER Bhopal
The matter at Neutron star (NS) cores are at highly compressed state and
due to gravity, the density can be built up to a few times the nuclear
saturation density. They are very compact and have been observationally
identified with pulsars with their mass being in the range from 0.7 - 3 solar
masses and a radius between 10-15 km. They are therefore one of the best
laboratories to test the theory of strong interaction at high-density low
temperature regimes. The information about the structure of an NS can be
given by their equation of states (EoSs). At high densities, the first
principle pQCD calculations are consistent and at lower densities field
theory calculations are consistent. The central density of the NS lies
somewhere between these two densities and in this regime the lattice QCD
calculations fail. Hence, we need to resort to model-based or agnostic
approaches to construct EoSs. In this talk, I will present how we can
effectively create several new EoSs from the information on the speed of
sound. Using the created EoSs, we create several datasets to train our
neural network. I will also talk about the neural network model using
which we can effectively predict a new EoS. Using these we study the
variation in the speed of sound inside the NS.
