Accelerating Stochastic Gravitational Wave Backgrounds Parameter Estimation in Pulsar Timing Arrays with Flow Matching

TL;DR

This paper introduces a flow-matching-based normalizing flow method to significantly accelerate and improve the accuracy of stochastic gravitational wave background parameter estimation in pulsar timing arrays, overcoming computational challenges of traditional techniques.

Contribution

The authors develop a novel flow-matching approach using continuous normalizing flows for efficient PTA analysis, achieving MCMC-level accuracy with drastically reduced computation time.

Findings

Achieves posterior distributions consistent with MCMC.

Reduces sampling time from 50 hours to 4 minutes.

Effectively scales to large datasets and prioritizes key parameters.

Abstract

Pulsar timing arrays (PTAs) are essential tools for detecting the stochastic gravitational wave background (SGWB), but their analysis faces significant computational challenges. Traditional methods like Markov-chain Monte Carlo (MCMC) struggle with high-dimensional parameter spaces where noise parameters often dominate, while existing deep learning approaches fail to model the Hellings-Downs (HD) correlation or are validated only on synthetic datasets. We propose a flow-matching-based continuous normalizing flow (CNF) for efficient and accurate PTA parameter estimation. By focusing on the 10 most contributive pulsars from the NANOGrav 15-year dataset, our method achieves posteriors consistent with MCMC, with a Jensen-Shannon divergence below \(10^{-2}\) nat, while reducing sampling time from 50 hours to 4 minutes. Powered by a versatile embedding network and a reweighting loss function, our approach prioritizes the SGWB parameters and scales effectively for future datasets. It enables precise reconstruction of SGWB and opens new avenues for exploring vast observational data and uncovering potential new physics, offering a transformative tool for advancing gravitational wave astronomy.