Generating Synthetic Cosmological Data with GalSampler
  As part of the effort to meet the needs of the Large Synoptic Survey
Telescope Dark Energy Science Collaboration (LSST DESC) for accurate,
realistically complex mock galaxy catalogs, we have developed GalSampler, an
open-source python package that assists in generating large volumes of
synthetic cosmological data. The key idea behind GalSampler is to recast
hydrodynamical simulations and semi-analytic models as physically-motivated
galaxy libraries. GalSampler populates a new, larger-volume halo catalog with
galaxies drawn from the baseline library; by using weighted sampling guided by
empirical modeling techniques, GalSampler inherits statistical accuracy from
the empirical model and physically-motivated complexity from the baseline
library. We have recently used GalSampler to produce the cosmoDC2 extragalactic
catalog made for the LSST DESC Data Challenge 2. Using cosmoDC2 as a guiding
example, we outline how GalSampler can continue to support ongoing and
near-future galaxy surveys such as the Dark Energy Survey (DES), the Dark
Energy Spectroscopic Instrument (DESI), WFIRST, and Euclid.
