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An emulator for the Lyman-alpha forest
Stockholm University, Faculty of Science, Department of Physics. Stockholm University, Faculty of Science, The Oskar Klein Centre for Cosmo Particle Physics (OKC).
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: 62019 (English)In: Journal of Cosmology and Astroparticle Physics, ISSN 1475-7516, E-ISSN 1475-7516, no 2, article id 050Article in journal (Refereed) Published
Abstract [en]

We present methods for interpolating between the 1-D flux power spectrum of the Lyman-alpha forest, as output by cosmological hydrodynamic simulations. Interpolation is necessary for cosmological parameter estimation due to the limited number of simulations possible. We construct an emulator for the Lyman-alpha forest flux power spectrum from 21 small simulations using Latin hypercube sampling and Gaussian process interpolation. We show that this emulator has a typical accuracy of 1 : 5% and a worst-case accuracy of 4%, which compares well to the current statistical error of 3-5% at z < 3 from BOSS DR9. We compare to the previous state of the art, quadratic polynomial interpolation. The Latin hypercube samples the entire volume of parameter space, while quadratic polynomial emulation samples only lower-dimensional subspaces. The Gaussian process provides an estimate of the emulation error and we show using test simulations that this estimate is reasonable. We construct a likelihood function and use it to show that the posterior constraints generated using the emulator are unbiased. We show that our Gaussian process emulator has lower emulation error than quadratic polynomial interpolation and thus produces tighter posterior confidence intervals, which will be essential for future Lyman-alpha surveys such as DESI.

Place, publisher, year, edition, pages
2019. no 2, article id 050
Keywords [en]
cosmological simulations, Lyman alpha forest, cosmological parameters from LSS
National Category
Physical Sciences
Identifiers
URN: urn:nbn:se:su:diva-167675DOI: 10.1088/1475-7516/2019/02/050ISI: 000459885700001OAI: oai:DiVA.org:su-167675DiVA, id: diva2:1301964
Available from: 2019-04-03 Created: 2019-04-03 Last updated: 2019-04-03Bibliographically approved

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Rogers, Keir K.Peiris, Hiranya V.
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