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Galaxy Formation: Bayesian History Matching for the Observable Universe

Vernon, Ian; Goldstein, Michael; Bower, Richard

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Authors

Michael Goldstein

Richard Bower



Abstract

Cosmologists at the Institute of Computational Cosmology, Durham University, have developed a state of the art model of galaxy formation known as Galform, intended to contribute to our understanding of the formation, growth and subsequent evolution of galaxies in the presence of dark matter. Galform requires the specification of many input parameters and takes a significant time to complete one simulation, making comparison between the model’s output and real observations of the Universe extremely challenging. This paper concerns the analysis of this problem using Bayesian emulation within an iterative history matching strategy, and represents the most detailed uncertainty analysis of a galaxy formation simulation yet performed.

Citation

Vernon, I., Goldstein, M., & Bower, R. (2014). Galaxy Formation: Bayesian History Matching for the Observable Universe. Statistical Science, 29(1), 81-90. https://doi.org/10.1214/12-sts412

Journal Article Type Article
Online Publication Date May 9, 2014
Publication Date Feb 1, 2014
Deposit Date Aug 19, 2014
Publicly Available Date Mar 28, 2024
Journal Statistical Science
Print ISSN 0883-4237
Electronic ISSN 2168-8745
Publisher Institute of Mathematical Statistics
Peer Reviewed Peer Reviewed
Volume 29
Issue 1
Pages 81-90
DOI https://doi.org/10.1214/12-sts412
Keywords Computer models, Bayesian statistics, History matching, Bayes linear, Emulation, Galaxy formation.

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