Skip to main content

Research Repository

Advanced Search

Galaxy Formation: a Bayesian Uncertainty Analysis

Vernon, Ian; Goldstein, Michael; Bower, Richard G.

Galaxy Formation: a Bayesian Uncertainty Analysis Thumbnail


Michael Goldstein

Richard G. Bower


In many scientific disciplines complex computer models are used to understand the behaviour of large scale physical systems. An uncertainty analysis of such a computer model known as Galform is presented. Galform models the creation and evolution of approximately one million galaxies from the beginning of the Universe until the current day, and is regarded as a state-of-the-art model within the cosmology community. It requires the specification of many input parameters in order to run the simulation, takes significant time to run, and provides various outputs that can be compared with real world data. A Bayes Linear approach is presented in order to identify the subset of the input space that could give rise to acceptable matches between model output and measured data. This approach takes account of the major sources of uncertainty in a consistent and unified manner, including input parameter uncertainty, function uncertainty, observational error, forcing function uncertainty and structural uncertainty. The approach is known as History Matching, and involves the use of an iterative succession of emulators (stochastic belief specifications detailing beliefs about the Galform function), which are used to cut down the input parameter space. The analysis was successful in producing a large collection of model evaluations that exhibit good fits to the observed data.


Vernon, I., Goldstein, M., & Bower, R. G. (2009). Galaxy Formation: a Bayesian Uncertainty Analysis. MUCM

Report Type Project Report
Acceptance Date Jul 1, 2009
Publication Date Jan 1, 2009
Deposit Date Jan 19, 2016
Publicly Available Date Oct 19, 2017
Series Title MUCM Technical Reports
Publisher URL
Additional Information University Name: Sheffield University
Publisher: MUCM
Type: monograph
Subtype: project_report


You might also like

Downloadable Citations