The anomaly of the CMB power with the latest Planck data

ArXiv ePrint: 2312.09989

Detalles Bibliográficos
Autores: Billi, Matteo, Barreiro, R. Belén, Martínez-González, Enrique
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/374041
Acceso en línea:http://hdl.handle.net/10261/374041
Access Level:acceso abierto
Palabra clave:CMBR polarisation
Frequentist statistics
Statistical sampling techniques
CMBR theory
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spelling The anomaly of the CMB power with the latest Planck dataBilli, MatteoBarreiro, R. BelénMartínez-González, EnriqueCMBR polarisationFrequentist statisticsStatistical sampling techniquesCMBR theoryArXiv ePrint: 2312.09989The lack of power anomaly is an unexpected feature observed at large angular scales in the maps of Cosmic Microwave Background (CMB) produced by the COBE, WMAP and Planck satellites. This signature, which consists in a missing of power with respect to that predicted by the ΛCDM model, might hint at a new cosmological phase before the standard inflationary era. The main point of this paper is taking into account the latest Planck polarisation data to investigate how the CMB polarisation improves the understanding of this feature. With this aim, we apply to the latest Planck data, both PR3 (2018) and PR4 (2020) releases, a new class of estimators capable of evaluating this anomaly by considering temperature and polarisation data both separately and in a jointly way. This is the first time that the PR4 dataset has been used to study this anomaly. To critically evaluate this feature, taking into account the residuals of known systematic effects present in the Planck datasets, we analyse the cleaned CMB maps using different combinations of sky masks, harmonic range and binning on the CMB multipoles. Our analysis shows that the estimator based only on temperature data confirms the presence of a lack of power with a lower-tail-probability (LTP), depending on the component separation method, ≤ 0.33% and ≤ 1.76% for PR3 and PR4, respectively. To our knowledge, the LTP≤ 0.33% for the PR3 dataset is the lowest one present in the literature obtained from Planck 2018 data, considering the Planck confidence mask. We find significant differences between these two datasets when polarisation is taken into account most likely due to a different level of systematics. Especially, the analysis with PR3 data, unlike that with PR4, seems to point towards a lack of power at large scales also for polarisation. Moreover, we also show that for the PR3 dataset the inclusion of the subdominant polarisation information provides estimates that are less likely accepted in a ΛCDM cosmological model than the only-temperature analysis over the entire harmonic-range considered. In particular, at ℓmax = 26, we found that no simulation has a value as low as the data for all the pipelines.MB would like to thank the Angela Della Riccia Foundation for the financial support provided under the ADR fellowships 2022 and 2023. We acknowledge partial financial support from the grant PID2019-110610RB-C21 funded by MCIN/AEI/10.13039/501100011033 and from the Red de Investigación RED2022-134715-T funded by MCIN/AEI/10.13039/5011000011033. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory, operated under Contract No. DEAC02-05CH11231.Peer reviewedIOP PublishingFondazione Angelo Della RicciaMinisterio de Ciencia, Innovación y Universidades (España)Agencia Estatal de Investigación (España)Department of Energy (US)National Energy Research Scientific Computing Center (US)Lawrence Berkeley National LaboratoryConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202420242024info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/374041reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-110610RB-C21info:eu-repo/grantAgreement/AEI//RED2022-134715-Thttps://doi.org/10.1088/1475-7516/2024/07/080Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3740412026-05-22T06:33:51Z
dc.title.none.fl_str_mv The anomaly of the CMB power with the latest Planck data
title The anomaly of the CMB power with the latest Planck data
spellingShingle The anomaly of the CMB power with the latest Planck data
Billi, Matteo
CMBR polarisation
Frequentist statistics
Statistical sampling techniques
CMBR theory
title_short The anomaly of the CMB power with the latest Planck data
title_full The anomaly of the CMB power with the latest Planck data
title_fullStr The anomaly of the CMB power with the latest Planck data
title_full_unstemmed The anomaly of the CMB power with the latest Planck data
title_sort The anomaly of the CMB power with the latest Planck data
dc.creator.none.fl_str_mv Billi, Matteo
Barreiro, R. Belén
Martínez-González, Enrique
author Billi, Matteo
author_facet Billi, Matteo
Barreiro, R. Belén
Martínez-González, Enrique
author_role author
author2 Barreiro, R. Belén
Martínez-González, Enrique
author2_role author
author
dc.contributor.none.fl_str_mv Fondazione Angelo Della Riccia
Ministerio de Ciencia, Innovación y Universidades (España)
Agencia Estatal de Investigación (España)
Department of Energy (US)
National Energy Research Scientific Computing Center (US)
Lawrence Berkeley National Laboratory
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv CMBR polarisation
Frequentist statistics
Statistical sampling techniques
CMBR theory
topic CMBR polarisation
Frequentist statistics
Statistical sampling techniques
CMBR theory
description ArXiv ePrint: 2312.09989
publishDate 2024
dc.date.none.fl_str_mv 2024
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/374041
url http://hdl.handle.net/10261/374041
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-110610RB-C21
info:eu-repo/grantAgreement/AEI//RED2022-134715-T
https://doi.org/10.1088/1475-7516/2024/07/080
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IOP Publishing
publisher.none.fl_str_mv IOP Publishing
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
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