## Statistical Science

### Galaxy Formation: Bayesian History Matching for the Observable Universe

#### 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.

#### Article information

Source
Statist. Sci., Volume 29, Number 1 (2014), 81-90.

Dates
First available in Project Euclid: 9 May 2014

https://projecteuclid.org/euclid.ss/1399645731

Digital Object Identifier
doi:10.1214/12-STS412

Mathematical Reviews number (MathSciNet)
MR3201849

Zentralblatt MATH identifier
1332.85007

#### Citation

Vernon, Ian; Goldstein, Michael; Bower, Richard. Galaxy Formation: Bayesian History Matching for the Observable Universe. Statist. Sci. 29 (2014), no. 1, 81--90. doi:10.1214/12-STS412. https://projecteuclid.org/euclid.ss/1399645731

#### References

• Bower, R. G., Benson, A. J. et al. (2006). The broken hierarchy of galaxy formation. Mon. Not. Roy. Astron. Soc. 370 645–655.
• Bower, R. G., Benson, A. J. et al. (2012). What shapes the galaxy mass function? Exploring the roles of supernova-driven winds and AGN. Mon. Not. Roy. Astron. Soc. 422 2816.
• Bower, R. G., Vernon, I., Goldstein, M. et al. (2010). The parameter space of galaxy formation. Mon. Not. Roy. Astron. Soc. 407 2017–2045.
• Craig, P. S., Goldstein, M., Seheult, A. H. and Smith, J. A. (1997). Pressure matching for hydrocarbon reservoirs: A case study in the use of Bayes linear strategies for large computer experiments. In Case Studies in Bayesian Statistics (C. Gatsonis, J. S. Hodges, R. E. Kass, R. McCulloch, P. Rossi and N. D. Singpurwalla, eds.) 3 36–93. Springer, New York.
• Goldstein, M. and Rougier, J. (2009). Reified Bayesian modelling and inference for physical systems (with discussion). J. Statist. Plann. Inference 139 1221–1239.
• Goldstein, M., Seheult, A. and Vernon, I. (2013). Assessing model adequacy. In Environmental Modelling: Finding Simplicity in Complexity Wiley, New York.
• Goldstein, M. and Vernon, I. (2009). Bayes linear analysis of imprecision in computer models, with application to understanding the Universe. In ISIPTA’09: Proceedings of the 6th International Symposium on Imprecise Probability: Theories and Applications 441–450. SIPTA, Lugano, Switzerland.
• Goldstein, M. and Wooff, D. (2007). Bayes Linear Statistics: Theory and Methods. Wiley, Chichester.
• Norberg, P., Cole, S. et al. (2002). The 2dF galaxy redshift survey: The $b_{J}$-band galaxy luminosity function and survey selection function. Mon. Not. Roy. Astron. Soc. 336 907–934.
• O’Hagan, A. (2006). Bayesian analysis of computer code outputs: A tutorial. Reliability Engineering and System Safety 91 1290–1300.
• Vernon, I., Goldstein, M. and Bower, R. G. (2010). Galaxy formation: A Bayesian uncertainty analysis (with discussion). Bayesian Anal. 5 619–669.