EGG: hatching a mock Universe from empirical prescriptions
C. Schreiber, D. Elbaz, M. Pannella, E. Merlin, M. Castellano, A., Fontana, N. Bourne, K. Boutsia, F. Cullen, J. Dunlop, H. C. Ferguson, M. J., Michalowski, K. Okumura, P. Santini, X. W. Shu, T. Wang, C. White

TL;DR
EGG is an empirical galaxy generator that creates realistic mock catalogs and images for deep field studies, matching observations across multiple wavelengths using data-driven prescriptions.
Contribution
This work introduces EGG, a novel empirical tool for generating realistic galaxy catalogs and images based solely on observational data, without relying on theoretical models.
Findings
Accurately reproduces observed galaxy number counts across broad bands
Generates realistic fluxes, morphologies, and clustering in mock catalogs
Provides open-source resources for the community
Abstract
This paper introduces EGG, the Empirical Galaxy Generator, a tool designed within the ASTRODEEP collaboration to generate mock galaxy catalogs for deep fields with realistic fluxes and simple morphologies. The simulation procedure is based exclusively on empirical prescriptions -- rather than first principles -- to provide the most accurate match with observations at 0<z<7. In particular, we consider that galaxies can be either quiescent or star-forming, and use their stellar mass (M*) and redshift (z) as the fundamental properties from which all the other observables can be statistically derived. Drawing z and M* from the observed galaxy stellar mass functions, we associate a star formation rate (SFR) to each galaxy from the tight SFR-M* main sequence, while dust attenuation, optical colors and morphologies (including bulge-to-total ratios, sizes and aspect ratios) are obtained from…
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