Fast gravitational wave parameter estimation without compromises

TL;DR

This paper introduces a fast, flexible framework for gravitational wave parameter estimation that achieves real-time Bayesian inference using advanced likelihood techniques, differentiable waveforms, and gradient-based sampling.

Contribution

The authors develop a novel, high-performance framework combining likelihood heterodyning, differentiable waveforms, and normalizing flows for rapid Bayesian parameter estimation of gravitational waves.

Findings

Achieves parameter estimation within a minute for real events

Does not require pretraining or explicit reparameterizations

Framework is generalizable to higher-dimensional problems

Abstract

We present a lightweight, flexible, and high-performance framework for inferring the properties of gravitational-wave events. By combining likelihood heterodyning, automatically-differentiable and accelerator-compatible waveforms, and gradient-based Markov chain Monte Carlo (MCMC) sampling enhanced by normalizing flows, we achieve full Bayesian parameter estimation for real events like GW150914 and GW170817 within a minute of sampling time. Our framework does not require pretraining or explicit reparameterizations and can be generalized to handle higher dimensional problems. We present the details of our implementation and discuss trade-offs and future developments in the context of other proposed strategies for real-time parameter estimation. Our code for running the analysis is publicly available on GitHub https://github.com/kazewong/jim.