We use cookies to ensure that we give you the best experience on our website. You can change your cookie settings at any time. Otherwise, we'll assume you're OK to continue.

Durham University

Department of Mathematical Sciences


Publication details for Ian Vernon

Vernon, Ian, Goldstein, Michael & Bower, Richard G. (2009). Galaxy Formation: a Bayesian Uncertainty Analysis. Sheffield MUCM.

Author(s) from Durham


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.