Fast Gravitational-wave Parameter Estimation without Compromises

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 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 at https://github.com/kazewong/jim.

Automated summary

This paper presents a lightweight, flexible, and high-performance framework for inferring the properties of gravitational-wave events. By employing likelihood heterodyning, automatically differentiable and accelerator-compatible waveforms, and gradient-based Markov Chain Monte Carlo sampling enhanced by normalizing flows, the authors achieve full Bayesian parameter estimation for real events within a minute of sampling time.