Euclid: modelling massive neutrinos in cosmology

• Verfasst von: Adamek, Julian [VerfasserIn]
• Verfasst von: Amendola, Luca [VerfasserIn]
• Verfasst von: Jahnke, Knud [VerfasserIn]
• Verfasst von: Seidel, Gregor [VerfasserIn]
• Titel: Euclid: modelling massive neutrinos in cosmology
• Titelzusatz: a code comparison
• Verf.angabe: on behalf of Euclid consortium*
• E-Jahr: 2023
• Jahr: 16 June 2023
• Umfang: 50 S.
• Illustrationen: Illustrationen
• Fussnoten: Veröffentlicht: 16. Juni 2023 ; *Euclid consortium: J. Adamek, L. Amendola, K. Jahnke, G. Seidel [und viele weitere Personen] ; Gesehen am 30.01.2024
• Titel Quelle: Enthalten in: Journal of cosmology and astroparticle physics
• Ort Quelle: London : IOP, 2003
• Jahr Quelle: 2023
• Band/Heft Quelle: (2023), 6, Artikel-ID 035, Seite 1-50
• ISSN Quelle: 1475-7516
• DOI: doi:10.1088/1475-7516/2023/06/035
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1879520095

Abstract

The measurement of the absolute neutrino mass scale from cosmological large-scale clustering data is one of the key science goals of the Euclid mission. Such a measurement relies on precise modelling of the impact of neutrinos on structure formation, which can be studied with N-body simulations. Here we present the results from a major code comparison effort to establish the maturity and reliability of numerical methods for treating massive neutrinos. The comparison includes eleven full N-body implementations (not all of them independent), two N-body schemes with approximate time integration, and four additional codes that directly predict or emulate the matter power spectrum. Using a common set of initial data we quantify the relative agreement on the nonlinear power spectrum of cold dark matter and baryons and, for the N-body codes, also the relative agreement on the bispectrum, halo mass function, and halo bias. We find that the different numerical implementations produce fully consistent results. We can therefore be confident that we can model the impact of massive neutrinos at the sub-percent level in the most common summary statistics. We also provide a code validation pipeline for future reference.