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    LINNA: Likelihood Inference Neural Network Accelerator

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    Thumbnail
    Name:
    NNSampler.pdf
    Embargo:
    2024-01-13
    Size:
    1.310Mb
    Format:
    PDF
    Description:
    Final Accepted Manuscript
    Download
    Author
    To, Chun-Hao
    Rozo, Eduardo
    Krause, Elisabeth
    Wu, Hao-Yi
    Wechsler, Risa H. cc
    Salcedo, Andrés N.
    Affiliation
    Department of Physics, University of Arizona
    Department of Astronomy/Steward Observatory, University of Arizona
    Issue Date
    2023-01-13
    Keywords
    Astronomy and Astrophysics
    Bayesian reasoning
    cosmological parameters from LSS
    Machine learning
    Statistical sampling techniques
    
    Metadata
    Show full item record
    Publisher
    IOP Publishing
    Citation
    To, C. H., Rozo, E., Krause, E., Wu, H. Y., Wechsler, R. H., & Salcedo, A. N. (2023). LINNA: Likelihood Inference Neural Network Accelerator. Journal of Cosmology and Astroparticle Physics, 2023(01), 016.
    Journal
    Journal of Cosmology and Astroparticle Physics
    Rights
    © 2023 IOP Publishing Ltd and Sissa Medialab
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    Bayesian posterior inference of modern multi-probe cosmological analyses incurs massive computational costs. For instance, depending on the combinations of probes, a single posterior inference for the Dark Energy Survey (DES) data had a wall-clock time that ranged from 1 to 21 days using a state-of-the-art computing cluster with 100 cores. These computational costs have severe environmental impacts and the long wall-clock time slows scientific productivity. To address these difficulties, we introduce LINNA: the Likelihood Inference Neural Network Accelerator. Relative to the baseline DES analyses, LINNA reduces the computational cost associated with posterior inference by a factor of 8–50. If applied to the first-year cosmological analysis of Rubin Observatory's Legacy Survey of Space and Time (LSST Y1), we conservatively estimate that LINNA will save more than U.S. $300,000 on energy costs, while simultaneously reducing CO2 emission by 2,400 tons. To accomplish these reductions, LINNA automatically builds training data sets, creates neural network emulators, and produces a Markov chain that samples the posterior. We explicitly verify that LINNA accurately reproduces the first-year DES (DES Y1) cosmological constraints derived from a variety of different data vectors with our default code settings, without needing to retune the algorithm every time. Further, we find that LINNA is sufficient for enabling accurate and efficient sampling for LSST Y10 multi-probe analyses. We make LINNA publicly available at https://github.com/chto/linna, to enable others to perform fast and accurate posterior inference in contemporary cosmological analyses.
    Note
    12 month embargo; published online 13 January 2023
    EISSN
    1475-7516
    DOI
    10.1088/1475-7516/2023/01/016
    Version
    Final accepted manuscript
    ae974a485f413a2113503eed53cd6c53
    10.1088/1475-7516/2023/01/016
    Scopus Count
    Collections
    UA Faculty Publications

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