Fast Parameter Inference on Pulsar Timing Arrays with Normalizing Flows Article Swipe

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David Shih , Marat Freytsis , Stephen R. Taylor , Jeff A. Dror , Nolan Smyth ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2310.12209

Pulsar timing arrays (PTAs) perform Bayesian posterior inference with expensive MCMC methods. Given a dataset of ~10-100 pulsars and O(10^3) timing residuals each, producing a posterior distribution for the stochastic gravitational wave background (SGWB) can take days to a week. The computational bottleneck arises because the likelihood evaluation required for MCMC is extremely costly when considering the dimensionality of the search space. Fortunately, generating simulated data is fast, so modern simulation-based inference techniques can be brought to bear on the problem. In this paper, we demonstrate how conditional normalizing flows trained on simulated data can be used for extremely fast and accurate estimation of the SGWB posteriors, reducing the sampling time from weeks to a matter of seconds.

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Computer Science

Pulsar

Algorithm

Artificial Intelligence

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Bottleneck Inference Markov chain Monte Carlo Computer science Pulsar Bayesian inference Algorithm Estimator Initialization Bayesian probability Curse of dimensionality Parameter space Posterior probability Artificial intelligence Statistics Mathematics Astrophysics Physics Embedded system Programming language

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2310.12209
PDF: https://arxiv.org/pdf/2310.12209
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DOI: https://doi.org/10.48550/arxiv.2310.12209

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Title: Fast Parameter Inference on Pulsar Timing Arrays with Normalizing Flows

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Language: en

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Publication year: 2023

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Publication date: 2023-10-18

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Authors: David Shih, Marat Freytsis, Stephen R. Taylor, Jeff A. Dror, Nolan Smyth

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Landing page: https://arxiv.org/abs/2310.12209

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Concepts: Bottleneck, Inference, Markov chain Monte Carlo, Computer science, Pulsar, Bayesian inference, Algorithm, Estimator, Initialization, Bayesian probability, Curse of dimensionality, Parameter space, Posterior probability, Artificial intelligence, Statistics, Mathematics, Astrophysics, Physics, Embedded system, Programming language

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