Nested sampling with normalizing flows for gravitational-wave inference

Williams, M. J. , Veitch, J. and Messenger, C. (2021) Nested sampling with normalizing flows for gravitational-wave inference. Physical Review D, 103(10), 103006. (doi: 10.1103/PhysRevD.103.103006)

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Abstract

We present a novel method for sampling iso-likelihood contours in nested sampling using a type of machine learning algorithm known as normalizing flows and incorporate it into our sampler nessai. nessai is designed for problems where computing the likelihood is computationally expensive and therefore the cost of training a normalizing flow is offset by the overall reduction in the number of likelihood evaluations. We validate our sampler on 128 simulated gravitational wave signals from compact binary coalescence and show that it produces unbiased estimates of the system parameters. Subsequently, we compare our results to those obtained with dynesty and find good agreement between the computed log-evidences while requiring 2.07 times fewer likelihood evaluations. We also highlight how the likelihood evaluation can be parallelized in nessai without any modifications to the algorithm. Finally, we outline diagnostics included in nessai and how these can be used to tune the sampler’s settings.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Veitch, Dr John and Williams, Michael and Messenger, Dr Christopher
Authors: Williams, M. J., Veitch, J., and Messenger, C.
College/School:College of Science and Engineering > School of Physics and Astronomy
Journal Name:Physical Review D
Publisher:American Physical Society
ISSN:2470-0010
ISSN (Online):2470-0029
Published Online:05 May 2021
Copyright Holders:Copyright © 2021 American Physical Society
First Published:First published in Physical Review D 103(10): 103006
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
169451Investigations in Gravitational RadiationSheila RowanScience and Technology Facilities Council (STFC)ST/L000946/1P&S - Physics & Astronomy