A Bayesian approach to the semi-analytic model of galaxy formation: methodology

Details A Bayesian approach to the semi-analytic model of galaxy formation: methodology A Bayesian approach to the semi-analytic model of galaxy formation: methodology

2010-04-14

arxiv.org/abs/1004.2518v3

MNRAS 416 (2011) 1949L

Astronomy and Astrophysics

Astrophysics

Cosmology and Extragalactic Astrophysics

revised version to match published article published in MNRAS

Scientific paper

10.1111/j.1365-2966.2011.19170.x

We believe that a wide range of physical processes conspire to shape the observed galaxy population but we remain unsure of their detailed interactions. The semi-analytic model (SAM) of galaxy formation uses multi-dimensional parameterisations of the physical processes of galaxy formation and provides a tool to constrain these underlying physical interactions. Because of the high dimensionality, the parametric problem of galaxy formation may be profitably tackled with a Bayesian-inference based approach, which allows one to constrain theory with data in a statistically rigorous way. In this paper we develop a SAM in the framework of Bayesian inference. We show that, with a parallel implementation of an advanced Markov-Chain Monte-Carlo algorithm, it is now possible to rigorously sample the posterior distribution of the high-dimensional parameter space of typical SAMs. As an example, we characterise galaxy formation in the current $\Lambda$CDM cosmology using the stellar mass function of galaxies as an observational constraint. We find that the posterior probability distribution is both topologically complex and degenerate in some important model parameters, suggesting that thorough explorations of the parameter space are needed to understand the models. We also demonstrate that because of the model degeneracy, adopting a narrow prior strongly restricts the model. Therefore, the inferences based on SAMs are conditional to the model adopted. Using synthetic data to mimic systematic errors in the stellar mass function, we demonstrate that an accurate observational error model is essential to meaningful inference.

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