Speaker
Description
We present a Machine Learning approach to perform fully Bayesian
inference of the neutron star equation of state given results from
parameter estimation from gravitational wave signals of binary neutron
star (BNS) mergers. The detection of gravitational waves from BNS merger
GW170817 during the second observing run of the ground based
gravitational wave detector network provided a new medium through which
to probe the neutron star equation of state. With the increased
sensitivity of the current and future observing runs, we expect to
detect more of such signals and therefore further constrain the equation
of state. Traditionally, equation of state inference is computationally
expensive and as such there is a need to improve analysis efficiency for
future observing runs. Our analysis facilitates both model-independent
and rapid equation of state inference to complement electromagnetic
follow-up investigation of gravitational wave events. Using a
conditional Normalising Flow, we can return O(1000) neutron star
equations of state given mass and tidal deformability samples in O(0.1)
seconds. We also discuss strategies for rapid hierarchical inference of
the dense matter equation of state from multiple gravitational wave events.
