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Bayesian emulator optimisation for cosmology: application to 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). University College London, U.K.ORCID iD: 0000-0002-2519-584X
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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 031Article in journal (Refereed) Published
Abstract [en]

The Lyman-alpha forest provides strong constraints on both cosmological parameters and intergalactic medium astrophysics, which are forecast to improve further with the next generation of surveys including eBOSS and DESI. As is generic in cosmological inference, extracting this information requires a likelihood to be computed throughout a high-dimensional parameter space. Evaluating the likelihood requires a robust and accurate mapping between the parameters and observables, in this case the 1D flux power spectrum. Cosmological simulations enable such a mapping, but due to computational time constraints can only be evaluated at a handful of sample points; emulators are designed to interpolate between these. The problem then reduces to placing the sample points such that an accurate mapping is obtained while minimising the number of expensive simulations required. To address this, we introduce an emulation procedure that employs Bayesian optimisation of the training set for a Gaussian process interpolation scheme. Starting with a Latin hypercube sampling (other schemes with good space-filling properties can be used), we iteratively augment the training set with extra simulations at new parameter positions which balance the need to reduce interpolation error while focussing on regions of high likelihood. We show that smaller emulator error from the Bayesian optimisation propagates to smaller widths on the posterior distribution. Even with fewer simulations than a Latin hypercube, Bayesian optimisation shrinks the 95% credible volume by 90% and, e.g., the 1 sigma error on the amplitude of small-scale primordial fluctuations by 38%. This is the first demonstration of Bayesian optimisation applied to large-scale structure emulation, and we anticipate the technique will generalise to many other probes such as galaxy clustering, weak lensing and 21cm.

Place, publisher, year, edition, pages
2019. no 2, article id 031
Keywords [en]
cosmological parameters from LSS, cosmological simulations, Lyman alpha forest
National Category
Physical Sciences
Identifiers
URN: urn:nbn:se:su:diva-166560DOI: 10.1088/1475-7516/2019/02/031ISI: 000458907100005OAI: oai:DiVA.org:su-166560DiVA, id: diva2:1293300
Available from: 2019-03-04 Created: 2019-03-04 Last updated: 2019-12-09Bibliographically approved

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Rogers, Keir K.Peiris, Hiranya V.
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