Generating Synthetic Cosmological Data with GalSampler

TL;DR

GalSampler is an open-source Python tool that generates large, realistic synthetic cosmological galaxy catalogs by combining empirical modeling with physically-motivated galaxy libraries, aiding upcoming large-scale surveys.

Contribution

The paper introduces GalSampler, a novel method that recasts simulations as galaxy libraries and uses weighted sampling to produce accurate, complex synthetic data for cosmological surveys.

Findings

Successfully produced the cosmoDC2 galaxy catalog for LSST DESC Data Challenge 2.

Demonstrated GalSampler's applicability to ongoing and future galaxy surveys.

Showcased how GalSampler combines empirical modeling with physical realism.

Abstract

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.