The dark energy survey 5-yr photometrically identified type Ia supernovae

As part of the cosmology analysis using Type Ia Supernovae (SN Ia) in the Dark Energy Survey (DES), we present photometrically identified SN Ia samples using multiband light curves and host galaxy redshifts. For this analysis, we use the photometric classification framework SUPERNNOVAtrained on real...

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Autores: Möller, Anais, Galbany, Lluís, Carretero, Jorge, Castander, Francisco J., Costanzi, M., Crocce, Martín, García-Bellido, Juan, Serrano, Santiago, Sevilla-Noarbe, I.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/295944
Acceso en línea:http://hdl.handle.net/10261/295944
Access Level:acceso abierto
Palabra clave:Methods: data analysis
Surveys
Supernovae: general
Cosmology: observations
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dc.title.none.fl_str_mv The dark energy survey 5-yr photometrically identified type Ia supernovae
title The dark energy survey 5-yr photometrically identified type Ia supernovae
spellingShingle The dark energy survey 5-yr photometrically identified type Ia supernovae
Möller, Anais
Methods: data analysis
Surveys
Supernovae: general
Cosmology: observations
title_short The dark energy survey 5-yr photometrically identified type Ia supernovae
title_full The dark energy survey 5-yr photometrically identified type Ia supernovae
title_fullStr The dark energy survey 5-yr photometrically identified type Ia supernovae
title_full_unstemmed The dark energy survey 5-yr photometrically identified type Ia supernovae
title_sort The dark energy survey 5-yr photometrically identified type Ia supernovae
dc.creator.none.fl_str_mv Möller, Anais
Galbany, Lluís
Carretero, Jorge
Castander, Francisco J.
Costanzi, M.
Crocce, Martín
García-Bellido, Juan
Serrano, Santiago
Sevilla-Noarbe, I.
author Möller, Anais
author_facet Möller, Anais
Galbany, Lluís
Carretero, Jorge
Castander, Francisco J.
Costanzi, M.
Crocce, Martín
García-Bellido, Juan
Serrano, Santiago
Sevilla-Noarbe, I.
author_role author
author2 Galbany, Lluís
Carretero, Jorge
Castander, Francisco J.
Costanzi, M.
Crocce, Martín
García-Bellido, Juan
Serrano, Santiago
Sevilla-Noarbe, I.
author2_role author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Ministerio de Ciencia, Innovación y Universidades (España)
Agencia Estatal de Investigación (España)
European Commission
European Research Council
Generalitat de Catalunya
Consejo Superior de Investigaciones Científicas (España)
Ministerio de Economía y Competitividad (España)
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Methods: data analysis
Surveys
Supernovae: general
Cosmology: observations
topic Methods: data analysis
Surveys
Supernovae: general
Cosmology: observations
description As part of the cosmology analysis using Type Ia Supernovae (SN Ia) in the Dark Energy Survey (DES), we present photometrically identified SN Ia samples using multiband light curves and host galaxy redshifts. For this analysis, we use the photometric classification framework SUPERNNOVAtrained on realistic DES-like simulations. For reliable classification, we process the DES SN programme (DES-SN) data and introduce improvements to the classifier architecture, obtaining classification accuracies of more than 98 per cent on simulations. This is the first SN classification to make use of ensemble methods, resulting in more robust samples. Using photometry, host galaxy redshifts, and a classification probability requirement, we identify 1863 SNe Ia from which we select 1484 cosmology-grade SNe Ia spanning the redshift range of 0.07 < z < 1.14. We find good agreement between the light-curve properties of the photometrically selected sample and simulations. Additionally, we create similar SN Ia samples using two types of Bayesian Neural Network classifiers that provide uncertainties on the classification probabilities. We test the feasibility of using these uncertainties as indicators for out-of-distribution candidates and model confidence. Finally, we discuss the implications of photometric samples and classification methods for future surveys such as Vera C. Rubin Observatory Legacy Survey of Space and Time.
publishDate 2022
dc.date.none.fl_str_mv 2022
2023
2023
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dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/295944
url http://hdl.handle.net/10261/295944
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Monthly Notices of the Royal Astronomical Society
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spelling The dark energy survey 5-yr photometrically identified type Ia supernovaeMöller, AnaisGalbany, LluísCarretero, JorgeCastander, Francisco J.Costanzi, M.Crocce, MartínGarcía-Bellido, JuanSerrano, SantiagoSevilla-Noarbe, I.Methods: data analysisSurveysSupernovae: generalCosmology: observationsAs part of the cosmology analysis using Type Ia Supernovae (SN Ia) in the Dark Energy Survey (DES), we present photometrically identified SN Ia samples using multiband light curves and host galaxy redshifts. For this analysis, we use the photometric classification framework SUPERNNOVAtrained on realistic DES-like simulations. For reliable classification, we process the DES SN programme (DES-SN) data and introduce improvements to the classifier architecture, obtaining classification accuracies of more than 98 per cent on simulations. This is the first SN classification to make use of ensemble methods, resulting in more robust samples. Using photometry, host galaxy redshifts, and a classification probability requirement, we identify 1863 SNe Ia from which we select 1484 cosmology-grade SNe Ia spanning the redshift range of 0.07 < z < 1.14. We find good agreement between the light-curve properties of the photometrically selected sample and simulations. Additionally, we create similar SN Ia samples using two types of Bayesian Neural Network classifiers that provide uncertainties on the classification probabilities. We test the feasibility of using these uncertainties as indicators for out-of-distribution candidates and model confidence. Finally, we discuss the implications of photometric samples and classification methods for future surveys such as Vera C. Rubin Observatory Legacy Survey of Space and Time.This paper has gone through internal review by the DES collaboration. 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 Collaborating Institutions are Argonne National Laboratory, the University of California at Santa Cruz, the University of Cambridge, Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas-Madrid, the University of Chicago, University College London, the DES-Brazil Consortium, the University of Edinburgh, the Eidgenössische Technische Hochschule Zürich (ETH), Fermi National Accelerator Laboratory, the University of Illinois at Urbana-Champaign, the Institut de Ciències de l’Espai (IEEC/CSIC), the Institut de Física d’Altes Energies, Lawrence Berkeley National Laboratory, the Ludwig-Maximilians Universität München and the associated Excellence Cluster Universe, the University of Michigan, NFS’s NOIRLab, the University of Nottingham, The Ohio State University, the University of Pennsylvania, the University of Portsmouth, SLAC National Accelerator Laboratory, Stanford University, the University of Sussex, Texas A&M University, and the OzDES Membership Consortium. Based in part on observations at Cerro Tololo Inter-American Observatory at NSF’s NOIRLab (NOIRLab Prop. ID 2012B-0001; PI: J. Frieman), which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation. 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). This work was completed in part with Midway resources provided by the University of Chicago’s Research Computing Center. This work makes use of data acquired at the Anglo-Australian Telescope, under program A/2013B/012. We acknowledge the traditional owners of the land on which the AAT stands, the Gamilaraay people, and pay our respects to elders past and present. MS is funded by the European Reearch Council (ERC) under the European Union’s Horizon 2020 Research and Innovation program (grant agreement no 759194 - USNAC). LG acknowledges financial support from the Spanish Ministerio de Ciencia e Innovación (MCIN), the Agencia Estatal de Investigación (AEI) 10.13039/501100011033, and the European Social Fund (ESF) ‘Investing in your future under the 2019 Ramón y Cajal program RYC2019-027683-I and the PID2020-115253GA-I00 HOSTFLOWS project, and from Centro Superior de Investigaciones Científicas (CSIC) under the PIE project 20215AT016. LK thanks the UKRI Future Leaders Fellowship for support through the grant MR/T01881X/1.Peer reviewedOxford University PressMinisterio de Ciencia, Innovación y Universidades (España)Agencia Estatal de Investigación (España)European CommissionEuropean Research CouncilGeneralitat de CatalunyaConsejo Superior de Investigaciones Científicas (España)Ministerio de Economía y Competitividad (España)Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202320232022info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/295944reponame:DIGITAL.CSIC. 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