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Galaxy formation: Bayesian history matching for the observable Universe. (English) Zbl 1332.85007
Summary: 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.

85A35 Statistical astronomy
62P35 Applications of statistics to physics
62F15 Bayesian inference
62-07 Data analysis (statistics) (MSC2010)
Full Text: DOI Euclid
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