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Free keywords:
Astrophysics, Instrumentation and Methods for Astrophysics, astro-ph.IM, Astrophysics, High Energy Astrophysical Phenomena, astro-ph.HE,General Relativity and Quantum Cosmology, gr-qc
Abstract:
The gravitational waves emitted by binary neutron star inspirals contain
information on nuclear matter above saturation density. However, extracting
this information and conducting parameter estimation remains a computationally
challenging and expensive task. Wong et al. introduced Jim arXiv:2302.05333, a
parameter estimation pipeline that combines relative binning and jax features
such as hardware acceleration and automatic differentiation into a normalizing
flow-enhanced sampler for gravitational waves from binary black hole (BBH)
mergers. In this work, we extend the Jim framework to analyze gravitational
wave signals from binary neutron stars (BNS) mergers with tidal effects
included. We demonstrate that Jim can be used for full Bayesian parameter
estimation of gravitational waves from BNS mergers within a few tens of
minutes, which includes the training of the normalizing flow and computing the
reference parameters for relative binning. For instance, Jim can analyze
GW170817 in 26 minutes (33 minutes) of total wall time using the TaylorF2
(IMRPhenomD_NRTidalv2) waveform, and GW190425 in around 21 minutes for both
waveforms. We highlight the importance of such an efficient parameter
estimation pipeline for several science cases as well as its ecologically
friendly implementation of gravitational wave parameter estimation.
