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A rigorous EFT-based forward model for large-scale structure
Stockholm University, Faculty of Science, Department of Physics. Stockholm University, Faculty of Science, The Oskar Klein Centre for Cosmo Particle Physics (OKC).ORCID iD: 0000-0002-4677-5843
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Number of Authors: 52019 (English)In: Journal of Cosmology and Astroparticle Physics, ISSN 1475-7516, E-ISSN 1475-7516, no 1, article id 042Article in journal (Refereed) Published
Abstract [en]

Conventional approaches to cosmology inference from galaxy redshift surveys are based on n-point functions, which are under rigorous perturbative control on sufficiently large scales. Here, we present an alternative approach, which employs a likelihood at the level of the galaxy density field. By integrating out small-scale modes based on effective-field theory arguments, we prove that this likelihood is under perturbative control if certain specific conditions are met. We further show that the information captured by this likelihood is equivalent to the combination of the next-to-leading order galaxy power spectrum, leading-order bispectrum, and BAO reconstruction. Combined with MCMC sampling and MAP optimization techniques, our results allow for fully Bayesian cosmology inference from large-scale structure that is under perturbative control. We illustrate this via a first demonstration of unbiased cosmology inference from nonlinear large-scale structure using this likelihood. In particular, we show unbiased estimates of the power spectrum normalization sigma(8) from a catalog of simulated dark matter halos, where nonlinear information is crucial in breaking the b(1) - sigma(8) degeneracy.

Place, publisher, year, edition, pages
2019. no 1, article id 042
Keywords [en]
baryon acoustic oscillations, cosmological parameters from LSS, galaxy clustering, redshift surveys
National Category
Physical Sciences
Identifiers
URN: urn:nbn:se:su:diva-166584DOI: 10.1088/1475-7516/2019/01/042ISI: 000456332900006OAI: oai:DiVA.org:su-166584DiVA, id: diva2:1293416
Available from: 2019-03-04 Created: 2019-03-04 Last updated: 2019-12-12Bibliographically approved

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Jasche, Jens
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