The Dark Energy Survey supernova program: Cosmological biases from supernova photometric classification

M. Vincenzi et al.

Detalles Bibliográficos
Autores: Vincenzi, Maria, Galbany, Lluís, Carretero, Jorge, Castander, Francisco J., Fosalba, Pablo, García-Bellido, Juan, Serrano, Santiago, DES Collaboration
Tipo de recurso: artículo
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:10261/349003
Acceso en línea:http://hdl.handle.net/10261/349003
Access Level:acceso abierto
Palabra clave:Supernovae: general
Cosmology: observations
Surveys
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dc.title.none.fl_str_mv The Dark Energy Survey supernova program: Cosmological biases from supernova photometric classification
title The Dark Energy Survey supernova program: Cosmological biases from supernova photometric classification
spellingShingle The Dark Energy Survey supernova program: Cosmological biases from supernova photometric classification
Vincenzi, Maria
Supernovae: general
Cosmology: observations
Surveys
title_short The Dark Energy Survey supernova program: Cosmological biases from supernova photometric classification
title_full The Dark Energy Survey supernova program: Cosmological biases from supernova photometric classification
title_fullStr The Dark Energy Survey supernova program: Cosmological biases from supernova photometric classification
title_full_unstemmed The Dark Energy Survey supernova program: Cosmological biases from supernova photometric classification
title_sort The Dark Energy Survey supernova program: Cosmological biases from supernova photometric classification
dc.creator.none.fl_str_mv Vincenzi, Maria
Galbany, Lluís
Carretero, Jorge
Castander, Francisco J.
Fosalba, Pablo
García-Bellido, Juan
Serrano, Santiago
DES Collaboration
author Vincenzi, Maria
author_facet Vincenzi, Maria
Galbany, Lluís
Carretero, Jorge
Castander, Francisco J.
Fosalba, Pablo
García-Bellido, Juan
Serrano, Santiago
DES Collaboration
author_role author
author2 Galbany, Lluís
Carretero, Jorge
Castander, Francisco J.
Fosalba, Pablo
García-Bellido, Juan
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)
European Research Council
European Commission
Agencia Estatal de Investigación (España)
Ministerio de Ciencia, Innovación y Universidades (España)
Ministerio de Ciencia e Innovación (España)
NASA
Ministerio de Economía y Competitividad (España)
Generalitat de Catalunya
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Supernovae: general
Cosmology: observations
Surveys
topic Supernovae: general
Cosmology: observations
Surveys
description M. Vincenzi et al.
publishDate 2023
dc.date.none.fl_str_mv 2023
2024
2024
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dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/349003
url http://hdl.handle.net/10261/349003
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The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI 10.1093/mnras/stac1404
https://doi.org/10.1093/mnras/stac1404
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publisher.none.fl_str_mv Oxford University Press
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spelling The Dark Energy Survey supernova program: Cosmological biases from supernova photometric classificationVincenzi, MariaGalbany, LluísCarretero, JorgeCastander, Francisco J.Fosalba, PabloGarcía-Bellido, JuanSerrano, SantiagoDES CollaborationSupernovae: generalCosmology: observationsSurveysM. Vincenzi et al.Cosmological analyses of samples of photometrically identified type Ia supernovae (SNe Ia) depend on understanding the effects of ‘contamination’ from core-collapse and peculiar SN Ia events. We employ a rigorous analysis using the photometric classifier SuperNNova on state-of-the-art simulations of SN samples to determine cosmological biases due to such ‘non-Ia’ contamination in the Dark Energy Survey (DES) 5-yr SN sample. Depending on the non-Ia SN models used in the SuperNNova training and testing samples, contamination ranges from 0.8 to 3.5 per cent, with a classification efficiency of 97.7–99.5 per cent. Using the Bayesian Estimation Applied to Multiple Species (BEAMS) framework and its extension BBC (‘BEAMS with Bias Correction’), we produce a redshift-binned Hubble diagram marginalized over contamination and corrected for selection effects, and use it to constrain the dark energy equation-of-state, w. Assuming a flat universe with Gaussian ΩM prior of 0.311 ± 0.010, we show that biases on w are <0.008 when using SuperNNova, with systematic uncertainties associated with contamination around 10 per cent of the statistical uncertainty on w for the DES-SN sample. An alternative approach of discarding contaminants using outlier rejection techniques (e.g. Chauvenet’s criterion) in place of SuperNNova leads to biases on w that are larger but still modest (0.015–0.03). Finally, we measure biases due to contamination on w0 and wa (assuming a flat universe), and find these to be <0.009 in w0 and <0.108 in wa, 5 to 10 times smaller than the statistical uncertainties for the DES-SN sample.This work was supported by the Science and Technology Facilities Council (grant number ST/P006760/1) through the DISCnet Centre for Doctoral Training. MS acknowledges support from EU/FP7-ERC grant 615929, and PW acknowledges support from STFC grant ST/R000506/1. TMD acknowledges support from ARC grant FL180100168. LG acknowledges financial support from the Spanish Ministry of Science, Innovation and Universities (MICIU) under the 2019 Ramón y Cajal program RYC2019-027683 and from the Spanish MICIU project PID2020-115253GA-I00. RH and MS were supported by DOE grant DE-FOA-0001781 and NASA grant NNH15ZDA001N-WFIRST. The material is based upon work supported by NASA under award number 80GSFC17M0002. LK thanks the UKRI Future Leaders Fellowship for support through the grant MR/T01881X/1. 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)European Research CouncilEuropean CommissionAgencia Estatal de Investigación (España)Ministerio de Ciencia, Innovación y Universidades (España)Ministerio de Ciencia e Innovación (España)NASAMinisterio de Economía y Competitividad (España)Generalitat de CatalunyaConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202420242023info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501application/pdfhttp://hdl.handle.net/10261/349003reponame:DIGITAL.CSIC. 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