Novel method for constraining the influence of dark matter hierarchical assembly on satellite distributions and other observable population statistics

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The influence of dark matter on galaxies is yet to be fully understood. Semi-Analytic, Semi-Empirical and hydrodynamical simulations use dark matter to inform the galaxy mergers, masses and positions. However, these simulations are limited in size and must therefore run a finite number of merger trees or a finite sized box. This limitation introduces inconsistencies or incompleteness in the total galaxy statistics particularly at high masses. We use a novel approach in empirical galaxy formation models forgoing the classical dark matter merger trees using instead a statistical outlook on the accretion history to be able to create less biased and more comprehensive statistics of the observables used to constrain galaxy populations. The model will be constrained using abundance matching, halo richness in Sloan digital sky survey and observed pair fraction/merger rates. We will then make predictions and test proposed models to create statistics on other observables such as galaxy morphologies, Sersic index, intra cluster light etc. This extensive project will be published in 3 papers: A methodology paper describing the constraints and the new approach. A second paper presenting predictions to be validated against a variety of data. A final paper where we use the new technique in tandem with other modelling methods.