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Primordial power spectrum and cosmology from black-box galaxy surveys
Stockholm University, Faculty of Science, Department of Physics. Stockholm University, Faculty of Science, The Oskar Klein Centre for Cosmo Particle Physics (OKC). Technische Universität München, Germany.
Number of Authors: 42019 (English)In: Monthly notices of the Royal Astronomical Society, ISSN 0035-8711, E-ISSN 1365-2966, Vol. 490, no 3, p. 4237-4253Article in journal (Refereed) Published
Abstract [en]

We propose a new, likelihood-free approach to inferring the primordial matter power spectrum and cosmological parameters from arbitrarily complex forward models of galaxy surveys where all relevant statistics can be determined from numerical simulations, i.e. black boxes. Our approach, which we call simulator expansion for likelihood-free inference (SELFI), builds upon approximate Bayesian computation using a novel effective likelihood, and upon the linearization of black-box models around an expansion point. Consequently, we obtain simple 'filter equations' for an effective posterior of the primordial power spectrum, and a straightforward scheme for cosmological parameter inference. We demonstrate that the workload is computationally tractable, fixed a priori, and perfectly parallel. As a proof of concept, we apply our framework to a realistic synthetic galaxy survey, with a data model accounting for physical structure formation and incomplete and noisy galaxy observations. In doing so, we show that the use of non-linear numerical models allows the galaxy power spectrum to be safely fitted up to at least k(max) = 0.5 h Mpc(-1), outperforming state-of-the-art backward-modelling techniques by a factor of similar to 5 in the number of modes used. The result is an unbiased inference of the primordial matter power spectrum across the entire range of scales considered, including a high-fidelity reconstruction of baryon acoustic oscillations. It translates into an unbiased and robust inference of cosmological parameters. Our results pave the path towards easy applications of likelihood-free simulation-based inference in cosmology.

Place, publisher, year, edition, pages
2019. Vol. 490, no 3, p. 4237-4253
Keywords [en]
methods: statistical, cosmological parameters, large-scale structure of Universe
National Category
Physical Sciences
Identifiers
URN: urn:nbn:se:su:diva-178719DOI: 10.1093/mnras/stz2718ISI: 000503993100083OAI: oai:DiVA.org:su-178719DiVA, id: diva2:1391427
Available from: 2020-02-04 Created: 2020-02-04 Last updated: 2020-02-04Bibliographically approved

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Leclercq, FlorentJasche, Jens
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