Modelling galaxy and black hole evolution via DECODE: Discrete statistical sEmi-empiriCal mODEl

Abstract

In a dark matter-dominated Universe, where structures grow hierarchically, galaxies are thought to live inside dark matter haloes with their evolution being intimately connected. In traditional models of galaxy evolution, galaxies build up their stellar mass via the interplay among several physical processes such as mergers, star formation and quenching, implying that all the observables characterizing galaxy evolution should be strictly linked to each other. These quantities include, for example, the galaxy stellar mass function, merger rates, star formation histories, satellite abundances and intracluster light. The failure in simultaneously fitting distinct observational probes may be attributed to shortcomings in the underlying modelling, or to disagreements in different data sets. Indeed, the systematic errors affecting observations still prevent universal and uniform measurements of, for instance, the stellar mass functions and the star formation rates, inevitably preventing theoretical models to compare with multiple data sets efficiently and simultaneously. Therefore, the need of well calibrated, homogeneous and self-consistent observational data sets is of vital importance for all types of theoretical galaxy evolution models, especially for data-driven models like semiempirical ones. The goal of this thesis is to build a holistic perspective among all the aforementioned quantities in galaxy evolution via a semi-empirical approach, exploring the role of each involved physical process. In this thesis, I will present DECODE, the Discrete statistical sEmi-empiriCal mODEl, and its contribution to the field of galaxy evolution. DECODE runs on top of objectby- object dark matter merger trees (hence discrete) generated from (sub)halo mass and infall redshifts distributions (hence statistical), without relying on full N-body simulations. Merger trees are then converted into galaxy assembly histories via abundance matching, using different stellar mass functions as input. First, I will apply DECODE to show the dependence of the output quantities on the input stellar mass function, probing that only specific characteristics of the latter can predict galaxy merger rates and star formation histories self-consistently with the latest observational data sets. Secondly, I will also show how DECODE can also efficiently predict other observables via simple merger models, such as the elliptical abundances, bulge-to-total distributions and intracluster light. Finally, I will present an updated version of DECODE, which grows galaxies via input star formation rate functions along with physically motivated quenching prescriptions, and will show the role of various quenching mechanisms in the stellar mass assembly histories of galaxies.

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ORCID for Poshak Gandhi: orcid.org/0000-0003-3105-2615

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