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dc.contributor.authorTo, Chun-Hao
dc.contributor.authorRozo, Eduardo
dc.contributor.authorKrause, Elisabeth
dc.contributor.authorWu, Hao-Yi
dc.contributor.authorWechsler, Risa H.
dc.contributor.authorSalcedo, Andrés N.
dc.date.accessioned2023-02-20T22:29:23Z
dc.date.available2023-02-20T22:29:23Z
dc.date.issued2023-01-13
dc.identifier.citationTo, 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.en_US
dc.identifier.doi10.1088/1475-7516/2023/01/016
dc.identifier.urihttp://hdl.handle.net/10150/667913
dc.description.abstractBayesian 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.en_US
dc.language.isoenen_US
dc.publisherIOP Publishingen_US
dc.rights© 2023 IOP Publishing Ltd and Sissa Medialaben_US
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en_US
dc.subjectAstronomy and Astrophysicsen_US
dc.subjectBayesian reasoningen_US
dc.subjectcosmological parameters from LSSen_US
dc.subjectMachine learningen_US
dc.subjectStatistical sampling techniquesen_US
dc.titleLINNA: Likelihood Inference Neural Network Acceleratoren_US
dc.typeArticleen_US
dc.identifier.eissn1475-7516
dc.contributor.departmentDepartment of Physics, University of Arizonaen_US
dc.contributor.departmentDepartment of Astronomy/Steward Observatory, University of Arizonaen_US
dc.identifier.journalJournal of Cosmology and Astroparticle Physicsen_US
dc.description.note12 month embargo; published online 13 January 2023en_US
dc.description.collectioninformationThis 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.en_US
dc.eprint.versionFinal accepted manuscripten_US
dc.source.journaltitleJournal of Cosmology and Astroparticle Physics
dc.source.volume2023
dc.source.issue01
dc.source.beginpage016


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