MAMBO -- An empirical galaxy and AGN mock catalogue for the exploitation of future surveys
X. L\'opez-L\'opez, M. Bolzonella, L. Pozzetti, M. Salvato, L., Bisigello, A. Feltre, I.E. L\'opez, A. Viitanen, V. Allevato, A. Bongiorno,, G. Girelli, J. Buchner, S. Charlot, F. Ricci, C. Schreiber, and G. Zamorani

TL;DR
MAMBO is an empirical workflow that creates realistic galaxy and AGN mock catalogues from dark matter simulations, accurately reproducing observed properties and aiding future survey predictions.
Contribution
It introduces a novel, flexible empirical method to generate galaxy and AGN catalogues directly from dark matter simulations, matching multiple observational properties.
Findings
Successfully reproduces luminosity functions up to z~7
Accurately models optical/NIR colour diagrams
Predicts galaxy and AGN counts for Euclid survey
Abstract
Aims. We present MAMBO, a flexible and efficient workflow to build empirical galaxy and Active Galactic Nuclei (AGN) mock catalogues that reproduce the physical and observational properties of these sources. Methods. We start from simulated dark matter (DM) haloes, to preserve the link with the cosmic web, and we populate them with galaxies and AGN using abundance matching techniques. We follow an empirical methodology, using stellar mass functions (SMF), host galaxy AGN mass functions and AGN accretion rate distribution functions studied at different redshifts to assign, among other properties, stellar masses, the fraction of quenched galaxies, or the AGN activity (demography, obscuration, multiwavelength emission, etc.). Results. As a proof test, we apply the method to a Millennium DM lightcone of 3.14 up to redshift and down to stellar masses $\mathcal{M}…
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Taxonomy
TopicsAstronomy and Astrophysical Research · Astronomical Observations and Instrumentation · Gamma-ray bursts and supernovae
