Anarya Ray

Gravitational Waves(GW's) observed from the Binary Neutron Star(BNS) coalescence GW170817 have been used to probe the nature of extreme matter inside neutron stars. Inferring the Neutron Star (NS) Equation of State (EoS) from GW data involve multiple nested sampling or Parameter Estimation (PE) runs per event, making it computationally expensive. Given the number of expected BNS detections in the 4th observing run of LIGO-VIRGO (O4), this problem can only worsen. An alternative rapid EoS model comparison technique: GWXtreme (Ghosh et. al 2021) that reuses mass and tidal-deformability posterior samples, has been shown to significantly speed up EoS model comparison requiring only one PE run per event. In this work, we generalize this technique to incorporate inference of phenomenologically parametrized EoS models, motivated by the superiority of using such models in constraining the properties of NS matter from GW data, over known named EoS models. We perform our new, computationally cheaper analysis on both real and simulated sources, and demonstrate the accuracy of our results in comparison to full PE runs and injected EoS parameters respectively.