Learning Bayesian Posteriors with Neural Networks for Gravitational-Wave Inference

Alvin J. K. Chua and Michele Vallisneri
Phys. Rev. Lett. 124, 041102 – Published 28 January 2020

Abstract

We seek to achieve the holy grail of Bayesian inference for gravitational-wave astronomy: using deep-learning techniques to instantly produce the posterior p(θ|D) for the source parameters θ, given the detector data D. To do so, we train a deep neural network to take as input a signal + noise dataset (drawn from the astrophysical source-parameter prior and the sampling distribution of detector noise), and to output a parametrized approximation of the corresponding posterior. We rely on a compact representation of the data based on reduced-order modeling, which we generate efficiently using a separate neural-network waveform interpolant [A. J. K. Chua, C. R. Galley, and M. Vallisneri, Phys. Rev. Lett. 122, 211101 (2019)]. Our scheme has broad relevance to gravitational-wave applications such as low-latency parameter estimation and characterizing the science returns of future experiments.

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  • Received 23 September 2019

DOI:https://doi.org/10.1103/PhysRevLett.124.041102

© 2020 American Physical Society

Physics Subject Headings (PhySH)

Gravitation, Cosmology & Astrophysics

Authors & Affiliations

Alvin J. K. Chua* and Michele Vallisneri

  • Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California 91109, USA

  • *Alvin.J.Chua@jpl.nasa.gov
  • Michele.Vallisneri@jpl.nasa.gov

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Issue

Vol. 124, Iss. 4 — 31 January 2020

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