Rapid Hierarchical Inference of Neutron Star Equation of State from multiple Gravitational Wave Observations of Binary Neutron Star Coalescences

Understand

Bayesian hierarchical inference of phenomenological parameterized neutron star equations of state (EoS) from multiple gravitational wave observations of binary neutron star mergers is of fundamental importance in improving our understanding of neutron star structure, the general properties of matter at supra nuclear densities and the strong nuclear force.

• However, such an analysis is computationally costly as it is unable to re-use single-event EoS agnostic parameter estimation runs that are carried out regardless for generating gravitational wave transient catalogs.
• With the number of events expected to be observable during the 4th observing run (O4) of LIGO/Virgo/KAGRA, this problem can only be expected to worsen.
• We develop a novel and robust algorithm for rapid and computationally cheap hierarchical inference of parameterized EoSs from gravitational wave data which re-uses single event EoS agnostic parameter estimation samples to significantly reduce computational cost.