ﻻ يوجد ملخص باللغة العربية
Observational systematics complicate comparisons with theoretical models limiting understanding of galaxy evolution. In particular, different empirical determinations of the stellar mass function imply distinct mappings between the galaxy and halo masses, leading to diverse galaxy evolutionary tracks. Using our state-of-the-art STatistical sEmi-Empirical modeL, STEEL, we show fully self-consistent models capable of generating galaxy growth histories that simultaneously and closely agree with the latest data on satellite richness and star-formation rates at multiple redshifts and environments. Central galaxy histories are generated using the central halo mass tracks from state-of-the-art statistical dark matter accretion histories coupled to abundance matching routines. We show that too flat high-mass slopes in the input stellar-mass-halo-mass relations as predicted by previous works, imply non-physical stellar mass growth histories weaker than those implied by satellite accretion alone. Our best-fit models reproduce the satellite distributions at the largest masses and highest redshifts probed, the latest data on star formation rates and its bi-modality in the local Universe, and the correct fraction of ellipticals. Our results are important to predict robust and self-consistent stellar-mass-halo-mass relations and to generate reliable galaxy mock catalogues for the next generations of extra-galactic surveys such as Euclid and LSST.
Connecting the observed rest-ultraviolet (UV) luminosities of high-$z$ galaxies to their intrinsic luminosities (and thus star formation rates) requires correcting for the presence of dust. We bypass a common dust-correction approach that uses empiri
We present STEEL a STatistical sEmi-Empirical modeL designed to probe the distribution of satellite galaxies in groups and clusters. Our fast statistical methodology relies on tracing the abundances of central and satellite haloes via their mass func
We present a semi-analytic model of satellite galaxies, SatGen, which can generate large samples of satellite populations for a host halo of desired mass, redshift, and assembly history. The model combines dark-matter halo merger trees, empirical rel
We present a simple semi-numerical model designed to explore black hole growth and galaxy evolution. This method builds on a previous model for black hole accretion that uses a semi-numerical galaxy formation model and universal Eddington ratio distr
We explore the galaxy-galaxy merger rate with the empirical model for galaxy formation, Emerge. On average, we find that between $2$ per cent and $20$ per cent of massive galaxies ($log_{10}(m_{*}/M_{odot}) geq 10.3$) will experience a major merger p