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Cosmology inference from a biased density field using the EFT-based likelihood
Stockholm University, Faculty of Science, Department of Physics. Stockholm University, Faculty of Science, The Oskar Klein Centre for Cosmo Particle Physics (OKC).
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Number of Authors: 52020 (English)In: Journal of Cosmology and Astroparticle Physics, E-ISSN 1475-7516, no 1, article id 029Article in journal (Refereed) Published
Abstract [en]

The effective-field-theory (EFT) approach to the clustering of galaxies and other biased tracers allows for an isolation of the cosmological information that is protected by symmetries, in particular the equivalence principle, and thus is robust to the complicated dynamics of dark matter, gas, and stars on small scales. All existing implementations proceed by making predictions for the lowest-order n-point functions of biased tracers, as well as their covariance, and comparing with measurements. Recently, we presented an EFT-based expression for the conditional probability of the density field of a biased tracer given the matter density field, which in principle uses information from arbitrarily high order n-point functions. Here, we report results based on this likelihood by applying it to halo catalogs in real space, specifically an inference of the power spectrum normalization sigma(8). We include bias terms up to second order as well as the leading higher-derivative term. For a cutoff value of Lambda = 0.1 hMpc(-1), we recover the ground-truth value of sigma(8) to within 95% CL for different halo samples and redshifts. We discuss possible sources for the remaining systematic bias in sigma(8) as well as future developments.

Place, publisher, year, edition, pages
2020. no 1, article id 029
Keywords [en]
cosmological parameters from LSS, power spectrum, redshift surveys
National Category
Physical Sciences
Identifiers
URN: urn:nbn:se:su:diva-181997DOI: 10.1088/1475-7516/2020/01/029ISI: 000528025800030Scopus ID: 2-s2.0-85082007982OAI: oai:DiVA.org:su-181997DiVA, id: diva2:1432289
Available from: 2020-05-26 Created: 2020-05-26 Last updated: 2023-03-28Bibliographically approved

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Jasche, Jens

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