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.
@article{arxiv.2412.19169,
title = {Accelerating Stochastic Gravitational Wave Backgrounds Parameter Estimation in Pulsar Timing Arrays with Flow Matching},
author = {Bo Liang and Chang Liu and Tianyu Zhao and Minghui Du and Manjia Liang and Ruijun Shi and Hong Guo and Yuxiang Xu and Li-e Qiang and Peng Xu and Wei-Liang Qian and Ziren Luo},
journal= {arXiv preprint arXiv:2412.19169},
year = {2024}
}