Modeling the Galaxy-Halo Connection for Large-Volume Surveys

With large-volume surveys becoming the norm, it is increasingly important to accurately model the galaxy-halo connection and being able to create mock universes of galaxies - starting from dark matter halo catalogs - that reproduce with high-fidelity all the characteristics of a given experiment. This step is necessary, in order to safely interpret cosmological data and fully control systematic effects. We are developing a new Python-based tool which integrates several existing packages and allows one to reproduce many of the forms used to describe galaxy-halo models, ranging from halo occupation distribution (HOD) to abundance matching techniques, along with the characteristics of a given survey and the main testable observables. We are making the code parallel for high-performance parallel-architectures.