Constraints on galaxy formation models from the galaxy stellar mass function and its evolution
Luiz Felippe S. Rodrigues (1), Ian Vernon (2), Richard Bower (2) ((1), Newcastle University, (2) Durham University)

TL;DR
This study uses Bayesian methods to constrain galaxy formation model parameters based on the galaxy stellar mass function and its evolution, identifying key parameters that influence galaxy growth and feedback processes.
Contribution
It applies Bayesian Emulator techniques to efficiently explore and constrain semi-analytic galaxy formation models using observational data, revealing key parameter sensitivities.
Findings
GSMF strongly constrains star formation and feedback parameters.
A subset of models matches GSMF evolution up to redshift 1.5.
Most plausible models suggest higher feedback efficiency at earlier times.
Abstract
We explore the parameter space of the semi-analytic galaxy formation model GALFORM, studying the constraints imposed by measurements of the galaxy stellar mass function (GSMF) and its evolution. We use the Bayesian Emulator method to quickly eliminate vast implausible volumes of the parameter space and zoom in on the most interesting regions, allowing us to identify a set of models that match the observational data within model uncertainties. We find that the GSMF strongly constrains parameters related to quiescent star formation in discs, stellar and AGN feedback and threshold for disc instabilities, but weakly restricts other parameters. Constraining the model using local data alone does not usually select models that match the evolution of the GSMF well. Nevertheless, we show that a small subset of models provides acceptable match to GSMF data out to redshift 1.5. We explore the…
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