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α-Deep Probabilistic Inference (α-DPI): efficient uncertainty quantification from exoplanet astrometry to black hole feature extraction

Sun, He and Bouman, Katherine L. and Tiede, Paul and Wang, Jason J. and Blunt, Sarah and Mawet, Dimitri (2022) α-Deep Probabilistic Inference (α-DPI): efficient uncertainty quantification from exoplanet astrometry to black hole feature extraction. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20220125-215129959

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Abstract

Inference is crucial in modern astronomical research, where hidden astrophysical features and patterns are often estimated from indirect and noisy measurements. Inferring the posterior of hidden features, conditioned on the observed measurements, is essential for understanding the uncertainty of results and downstream scientific interpretations. Traditional approaches for posterior estimation include sampling-based methods and variational inference. However, sampling-based methods are typically slow for high-dimensional inverse problems, while variational inference often lacks estimation accuracy. In this paper, we propose α-DPI, a deep learning framework that first learns an approximate posterior using alpha-divergence variational inference paired with a generative neural network, and then produces more accurate posterior samples through importance re-weighting of the network samples. It inherits strengths from both sampling and variational inference methods: it is fast, accurate, and scalable to high-dimensional problems. We apply our approach to two high-impact astronomical inference problems using real data: exoplanet astrometry and black hole feature extraction.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/2201.08506arXivDiscussion Paper
ORCID:
AuthorORCID
Sun, He0000-0003-1526-6787
Bouman, Katherine L.0000-0003-0077-4367
Tiede, Paul0000-0003-3826-5648
Wang, Jason J.0000-0003-0774-6502
Blunt, Sarah0000-0002-3199-2888
Mawet, Dimitri0000-0002-8895-4735
Alternate Title:alpha-Deep Probabilistic Inference (alpha-DPI): efficient uncertainty quantification from exoplanet astrometry to black hole feature extraction
Additional Information:Attribution 4.0 International (CC BY 4.0). This work was supported by NSF award 1935980, NSF award 2034306, NSF award 2048237, and Amazon AI4Science Fellowship. We thank the National Science Foundation (AST-1935980) for financial support of this work. This work has been supported in part by the Black Hole Initiative at Harvard University, which is funded by grants from the John Templeton Foundation and the Gordon and Betty Moore Foundation to Harvard University. This work was supported in part by Perimeter Institute for Theoretical Physics. Research at Perimeter Institute is supported by the Government of Canada through the Department of Innovation, Science and Economic Development Canada and by the Province of Ontario through the Ministry of Economic Development, Job Creation and Trade. J.W. and S.B. are supported by the Heising-Simons Foundation (including grant 2019-1698). The authors would also like to thank Shiro Ikeda for the helpful discussions. Facilities: Gemini Planet Imager (GPI), Event Horizon Telescope (EHT) Software: astropy (Astropy Collaboration et al. 2013, 2018), Cloudy (Ferland et al. 2013), Source Extractor (Bertin & Arnouts 1996) eht-imaging (Chael et al. 2018)
Group:Astronomy Department
Funders:
Funding AgencyGrant Number
NSFAST-1935980
NSFAST-2034306
NSFCCF-2048237
Amazon AI4Science FellowshipUNSPECIFIED
Black Hole InitiativeUNSPECIFIED
John Templeton FoundationUNSPECIFIED
Gordon and Betty Moore FoundationUNSPECIFIED
Perimeter Institute for Theoretical PhysicsUNSPECIFIED
Department of Innovation, Science and Economic Development (Canada)UNSPECIFIED
Ontario Ministry of Economic Development, Job Creation and TradeUNSPECIFIED
Heising-Simons Foundation2019-1698
Subject Keywords:Uncertainty quantification, Bayesian inference, Normalizing flow, Event Horizon Telescope, Black hole imaging, Exoplanet direct imaging, Astrometry
Record Number:CaltechAUTHORS:20220125-215129959
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220125-215129959
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:113103
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:25 Jan 2022 22:14
Last Modified:25 Jan 2022 22:14

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