Robust sampling for weak lensing and clustering analyses with the Dark Energy Survey

P. Lemos et al.

Detalles Bibliográficos
Autores: Lemos, Pablo, Weaverdyck, Noah, Castander, Francisco J., Crocce, Martín, García-Bellido, Juan, Gaztañaga, Enrique, Serrano, Santiago, DES Collaboration
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/348637
Acceso en línea:http://hdl.handle.net/10261/348637
Access Level:acceso abierto
Palabra clave:Methods: statistical
Cosmological parameters
Cosmology: observations
Large-scale structure of the universe
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dc.title.none.fl_str_mv Robust sampling for weak lensing and clustering analyses with the Dark Energy Survey
title Robust sampling for weak lensing and clustering analyses with the Dark Energy Survey
spellingShingle Robust sampling for weak lensing and clustering analyses with the Dark Energy Survey
Lemos, Pablo
Methods: statistical
Cosmological parameters
Cosmology: observations
Large-scale structure of the universe
title_short Robust sampling for weak lensing and clustering analyses with the Dark Energy Survey
title_full Robust sampling for weak lensing and clustering analyses with the Dark Energy Survey
title_fullStr Robust sampling for weak lensing and clustering analyses with the Dark Energy Survey
title_full_unstemmed Robust sampling for weak lensing and clustering analyses with the Dark Energy Survey
title_sort Robust sampling for weak lensing and clustering analyses with the Dark Energy Survey
dc.creator.none.fl_str_mv Lemos, Pablo
Weaverdyck, Noah
Castander, Francisco J.
Crocce, Martín
García-Bellido, Juan
Gaztañaga, Enrique
Serrano, Santiago
DES Collaboration
author Lemos, Pablo
author_facet Lemos, Pablo
Weaverdyck, Noah
Castander, Francisco J.
Crocce, Martín
García-Bellido, Juan
Gaztañaga, Enrique
Serrano, Santiago
DES Collaboration
author_role author
author2 Weaverdyck, Noah
Castander, Francisco J.
Crocce, Martín
García-Bellido, Juan
Gaztañaga, Enrique
Serrano, Santiago
DES Collaboration
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Science and Technology Facilities Council (UK)
Department of Energy (US)
National Science Foundation (US)
Ministerio de Educación (España)
Generalitat de Catalunya
Agencia Estatal de Investigación (España)
Ministerio de Ciencia, Innovación y Universidades (España)
Ministerio de Economía y Competitividad (España)
Ministerio de Ciencia e Innovación (España)
European Commission
European Research Council
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Methods: statistical
Cosmological parameters
Cosmology: observations
Large-scale structure of the universe
topic Methods: statistical
Cosmological parameters
Cosmology: observations
Large-scale structure of the universe
description P. Lemos et al.
publishDate 2023
dc.date.none.fl_str_mv 2023
2024
2024
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publisher.none.fl_str_mv Oxford University Press
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spelling Robust sampling for weak lensing and clustering analyses with the Dark Energy SurveyLemos, PabloWeaverdyck, NoahCastander, Francisco J.Crocce, MartínGarcía-Bellido, JuanGaztañaga, EnriqueSerrano, SantiagoDES CollaborationMethods: statisticalCosmological parametersCosmology: observationsLarge-scale structure of the universeP. Lemos et al.Recent cosmological analyses rely on the ability to accurately sample from high-dimensional posterior distributions. A variety of algorithms have been applied in the field, but justification of the particular sampler choice and settings is often lacking. Here, we investigate three such samplers to motivate and validate the algorithm and settings used for the Dark Energy Survey (DES) analyses of the first 3 yr (Y3) of data from combined measurements of weak lensing and galaxy clustering. We employ the full DES Year 1 likelihood alongside a much faster approximate likelihood, which enables us to assess the outcomes from each sampler choice and demonstrate the robustness of our full results. We find that the ellipsoidal nested sampling algorithm MULTINEST reports inconsistent estimates of the Bayesian evidence and somewhat narrower parameter credible intervals than the sliced nested sampling implemented in POLYCHORD. We compare the findings from MULTINEST and POLYCHORD with parameter inference from the Metropolis–Hastings algorithm, finding good agreement. We determine that POLYCHORD provides a good balance of speed and robustness for posterior and evidence estimation, and recommend different settings for testing purposes and final chains for analyses with DES Y3 data. Our methodology can readily be reproduced to obtain suitable sampler settings for future surveys.PL acknowledges STFC Consolidated Grants ST/R000476/1 and ST/T000473/1. NW is supported by the Chamberlain fellowship at Lawrence Berkeley National Laboratory. This work was supported through computational resources and services provided by the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility operated under Contract No. DE-AC02-05CH11231; and by the Sherlock cluster, supported by Stanford University and the Stanford Research Computing Center. Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Científico e Tecnológico and the Ministério da Ciência, Tecnologia e Inovação, the Deutsche Forschungsgemeinschaft and the Collaborating Institutions in the Dark Energy Survey. The DES data management system is supported by the National Science Foundation under Grant Numbers AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MICINN under grants ESP2017-89838, PGC2018-094773, PGC2018-102021, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Program (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Brazilian Instituto Nacional de Ciência e Tecnologia (INCT) do e-Universo (CNPq grant 465376/2014-2).Peer reviewedOxford University PressScience and Technology Facilities Council (UK)Department of Energy (US)National Science Foundation (US)Ministerio de Educación (España)Generalitat de CatalunyaAgencia Estatal de Investigación (España)Ministerio de Ciencia, Innovación y Universidades (España)Ministerio de Economía y Competitividad (España)Ministerio de Ciencia e Innovación (España)European CommissionEuropean Research CouncilConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202420242023info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/348637reponame:DIGITAL.CSIC. 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