Robust parameter estimation within minutes on gravitational wave signals from binary neutron star inspirals

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 Astrophys. J. 958, 129 (2023), 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 mergers. In this work, we extend the jim framework to analyze gravitational wave signals from binary neutron star (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 20 min (31 min) of total wall time using the TaylorF2 (IMRPhenomDNRTidalv2) waveform, and GW190425 in around 21 min (25 min). 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.