Euclid preparation LII. Forecast impact of super-sample covariance on 3×2pt analysis with Euclid
[Context] Deviations from Gaussianity in the distribution of the fields probed by large-scale structure surveys generate additional terms in the data covariance matrix, increasing the uncertainties in the measurement of the cosmological parameters. Super-sample covariance (SSC) is among the largest...
| Autores: | , , , , , , , , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | España |
| Recursos: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/382282 |
| Acesso em linha: | http://hdl.handle.net/10261/382282 |
| Access Level: | acceso abierto |
| Palavra-chave: | Asteroseismology Cosmological parameters Cosmology: observations Cosmology: theory Dark energy Large-scale structure of Universe |
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Euclid preparation LII. Forecast impact of super-sample covariance on 3×2pt analysis with Euclid |
| title |
Euclid preparation LII. Forecast impact of super-sample covariance on 3×2pt analysis with Euclid |
| spellingShingle |
Euclid preparation LII. Forecast impact of super-sample covariance on 3×2pt analysis with Euclid Euclid Collaboration Asteroseismology Cosmological parameters Cosmology: observations Cosmology: theory Dark energy Large-scale structure of Universe |
| title_short |
Euclid preparation LII. Forecast impact of super-sample covariance on 3×2pt analysis with Euclid |
| title_full |
Euclid preparation LII. Forecast impact of super-sample covariance on 3×2pt analysis with Euclid |
| title_fullStr |
Euclid preparation LII. Forecast impact of super-sample covariance on 3×2pt analysis with Euclid |
| title_full_unstemmed |
Euclid preparation LII. Forecast impact of super-sample covariance on 3×2pt analysis with Euclid |
| title_sort |
Euclid preparation LII. Forecast impact of super-sample covariance on 3×2pt analysis with Euclid |
| dc.creator.none.fl_str_mv |
Euclid Collaboration Sciotti, Davide Gouyou Beauchamps, Sylvain Tutusaus, Isaac Castander, Francisco J. Fosalba, Pablo Serrano, Santiago Gaztañaga, Enrique Akrami, Yashar García-Bellido, Juan |
| author |
Euclid Collaboration |
| author_facet |
Euclid Collaboration Sciotti, Davide Gouyou Beauchamps, Sylvain Tutusaus, Isaac Castander, Francisco J. Fosalba, Pablo Serrano, Santiago Gaztañaga, Enrique Akrami, Yashar García-Bellido, Juan |
| author_role |
author |
| author2 |
Sciotti, Davide Gouyou Beauchamps, Sylvain Tutusaus, Isaac Castander, Francisco J. Fosalba, Pablo Serrano, Santiago Gaztañaga, Enrique Akrami, Yashar García-Bellido, Juan |
| author2_role |
author author author author author author author author author |
| dc.contributor.none.fl_str_mv |
European Research Council Ministerio de Ciencia, Innovación y Universidades (España) Agencia Estatal de Investigación (España) Sciotti, Davide [0009-0008-4519-2620] Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72] |
| dc.subject.none.fl_str_mv |
Asteroseismology Cosmological parameters Cosmology: observations Cosmology: theory Dark energy Large-scale structure of Universe |
| topic |
Asteroseismology Cosmological parameters Cosmology: observations Cosmology: theory Dark energy Large-scale structure of Universe |
| description |
[Context] Deviations from Gaussianity in the distribution of the fields probed by large-scale structure surveys generate additional terms in the data covariance matrix, increasing the uncertainties in the measurement of the cosmological parameters. Super-sample covariance (SSC) is among the largest of these non-Gaussian contributions, with the potential to significantly degrade constraints on some of the parameters of the cosmological model under study – especially for weak-lensing cosmic shear. |
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2024 |
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2024 2025 2025 |
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info:eu-repo/semantics/article http://purl.org/coar/resource_type/c_6501 Publisher's version info:eu-repo/semantics/publishedVersion |
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article |
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publishedVersion |
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http://hdl.handle.net/10261/382282 |
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http://hdl.handle.net/10261/382282 |
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Inglés |
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Inglés |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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EDP Sciences |
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EDP Sciences |
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reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC instname:Consejo Superior de Investigaciones Científicas (CSIC) |
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Euclid preparation LII. Forecast impact of super-sample covariance on 3×2pt analysis with EuclidEuclid CollaborationSciotti, DavideGouyou Beauchamps, SylvainTutusaus, IsaacCastander, Francisco J.Fosalba, PabloSerrano, SantiagoGaztañaga, EnriqueAkrami, YasharGarcía-Bellido, JuanAsteroseismologyCosmological parametersCosmology: observationsCosmology: theoryDark energyLarge-scale structure of Universe[Context] Deviations from Gaussianity in the distribution of the fields probed by large-scale structure surveys generate additional terms in the data covariance matrix, increasing the uncertainties in the measurement of the cosmological parameters. Super-sample covariance (SSC) is among the largest of these non-Gaussian contributions, with the potential to significantly degrade constraints on some of the parameters of the cosmological model under study – especially for weak-lensing cosmic shear.[Aims] We compute and validate the impact of SSC on the forecast uncertainties on the cosmological parameters for the Euclid photo-metric survey, and investigate how its impact depends on the specific details of the forecast.[Methods] We followed the recipes outlined by the Euclid Collaboration (EC) to produce 1σ constraints through a Fisher matrix analysis, considering the Gaussian covariance alone and adding the SSC term, which is computed through the public code PySSC. The constraints are produced both by using Euclid’s photometric probes in isolation and by combining them in the ‘3×2pt’ analysis.[Results] We meet EC requirements on the forecasts validation, with an agreement at the 10% level between the mean results of the two pipelines considered, and find the SSC impact to be non-negligible - halving the figure of merit (FoM) of the dark energy parameters (w0, wa) in the 3×2pt case and substantially increasing the uncertainties on Ωm,0,w0, w0, and σ8 for the weak-lensing probe. We find photometric galaxy clustering to be less affected as a consequence of the lower probe response. The relative impact of SSC, while highly dependent on the number and type of nuisance parameters varied in the analysis, does not show significant changes under variations of the redshift binning scheme. Finally, we explore how the use of prior information on the shear and galaxy bias changes the impact of SSC. We find that improving shear bias priors has no significant influence, while galaxy bias must be calibrated to a subpercent level in order to increase the FoM by the large amount needed to achieve the value when SSC is not included.The computational part of the work has been performed using the Python programming language, interfaced with scientific packages like astropy (Astropy Collaboration 2013, 2018) for cosmological calculations, Numba (Lam et al. 2015) for code speedup, NumPy (Harris et al. 2020) for matrix manipulation, SciPy (Virtanen et al. 2020) for numerical integration and Matplotlib (Hunter 2007) for data visualisation. The authors thank the anonymous referees for their helpful comments that improved the quality of the manuscript. DS would like to thank Raphael Kou for the fruitful discussion on the SSC impact on GCph. SGB was supported by CNES, focused on Euclid mission. The project leading to this publication has received funding from Excellence Initiative of Aix-Marseille University -A*MIDEX, a French “Investissements d’Avenir” programme (AMX-19-IET-008-IPhU). SC acknowledges support from the ‘Departments of Excellence 2018-2022’ Grant (L. 232/2016) awarded by the Italian Ministry of University and Research (MUR). IT acknowledges funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 863929; project title “Testing the law of gravity with novel large-scale structure observables” and acknowledges support from the Spanish Ministry of Science, Innovation and Universities through grant ESP2017-89838, and the H2020 programme of the European Commission through grant 776247. The Euclid Consortium acknowledges the European Space Agency and a number of agencies and institutes that have supported the development of Euclid, in particular the Agenzia Spaziale Italiana, the Austrian Forschungsförderungsgesellschaft funded through BMK, the Belgian Science Policy, the Canadian Euclid Consortium, the Deutsches Zentrum für Luft- und Raumfahrt, the DTU Space and the Niels Bohr Institute in Denmark, the French Centre National d’Etudes Spatiales, the Fundação para a Ciência e a Tecnologia, the Hungarian Academy of Sciences, the Ministerio de Ciencia, Innovación y Universidades, the National Aeronautics and Space Administration, the National Astronomical Observatory of Japan, the Netherlandse Onderzoekschool Voor Astronomie, the Norwegian Space Agency, the Research Council of Finland, the Romanian Space Agency, the State Secretariat for Education, Research, and Innovation (SERI) at the Swiss Space Office (SSO), and the United Kingdom Space Agency. A complete and detailed list is available on the Euclid web site (www.euclid-ec.org).Peer reviewedEDP SciencesEuropean Research CouncilMinisterio de Ciencia, Innovación y Universidades (España)Agencia Estatal de Investigación (España)Sciotti, Davide [0009-0008-4519-2620]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252024info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/382282reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/EC/HE/863929info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/ESP2017-89838info:eu-repo/grantAgreement/EC/H2020/776247https://doi.org/10.1051/0004-6361/202348389Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3822822026-05-22T06:33:51Z |
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