Galaxy Formation: Bayesian History Matching for the Observable Universe

Vernon, Ian; Goldstein, Michael; Bower, Richard

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: Apr 13, 2015
• Journal: Statistical Science
• Print ISSN: 0883-4237
• 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.
• Public URL: https://durham-repository.worktribe.com/output/1422097