Learning Bayesian Posteriors with Neural Networks for Gravitational-Wave Inference.

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ID: 96009
2020
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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)PRLTAO0031-900710.1103/PhysRevLett.122.211101]. Our scheme has broad relevance to gravitational-wave applications such as low-latency parameter estimation and characterizing the science returns of future experiments.
Reference Key
chua2020learningphysical Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Chua, Alvin J K;Vallisneri, Michele;
Journal physical review letters
Year 2020
DOI
10.1103/PhysRevLett.124.041102
URL
Keywords
Deep learning Neural network Defect detection continuous casting surface defects 3d imaging

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