Testing the key role of the stellar mass-halo mass relation in galaxy merger rates and morphologies via DECODE, a novel Discrete statistical sEmi-empiriCal mODEl
Hao Fu, Francesco Shankar, Mohammadreza Ayromlou, Max Dickson, Ioanna, Koutsouridou, Yetli Rosas-Guevara, Christopher Marsden, Kristina Brocklebank,, Mariangela Bernardi, Nikolaos Shiamtanis, Joseph Williams, Lorenzo Zanisi,, Viola Allevato, Lumen Boco, Silvia Bonoli

TL;DR
DECODE is a new semi-empirical model that efficiently predicts galaxy assembly and merger histories based on the stellar mass-halo mass relation, revealing its critical role in galaxy evolution and morphology.
Contribution
The paper introduces DECODE, a novel discrete semi-empirical model that accurately predicts galaxy merger and assembly histories using input SMHM relations, with no resolution limits.
Findings
SMHM relations with high massive galaxy abundance reproduce satellite and merger data.
Models matching observed galaxy growth require significant redshift evolution in SMHM relations.
Major mergers can explain the formation of elliptical galaxies, but not all bulge-to-disc ratios.
Abstract
The relative roles of mergers and star formation in regulating galaxy growth are still a matter of intense debate. We here present our DECODE, a new Discrete statistical sEmi-empiriCal mODEl specifically designed to predict rapidly and efficiently, in a full cosmological context, galaxy assembly and merger histories for any given input stellar mass-halo mass (SMHM) relation. DECODE generates object-by-object dark matter merger trees (hence discrete) from accurate subhalo mass and infall redshift probability functions (hence statistical) for all subhaloes, including those residing within other subhaloes, with virtually no resolution limits on mass or volume. Merger trees are then converted into galaxy assembly histories via an input, redshift dependent SMHM relation, which is highly sensitive to the significant systematics in the galaxy stellar mass function and on its evolution with…
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