This paper introduces cosmoDC2, a large synthetic galaxy catalog designed to support precision dark energy science with the Large Synoptic Survey Telescope (LSST). CosmoDC2 is the starting point for the second data challenge (DC2) carried out by the LSST Dark Energy Science Collaboration (LSST DESC). The catalog is based on a trillion-particle, (4.225 Gpc) 3 box cosmological N-body simulation, the 'Outer Rim' run. It covers 440 deg 2 of sky area to a redshift of z = 3 and is complete to a magnitude depth of 28 in the r-band. Each galaxy is characterized by a multitude of properties including stellar mass, morphology, spectral energy distributions, broadband filter magnitudes, host halo information and weak lensing shear. The size and complexity of cosmoDC2 requires an efficient catalog generation methodology; our approach is based on a new hybrid technique that combines data-driven empirical approaches with semi-analytic galaxy modeling. A wide range of observation-based validation tests has been implemented to ensure that cosmoDC2 enables the science goals of the planned LSST DESC DC2 analyses. This paper also represents the official release of the cosmoDC2 data set, including an efficient reader that facilitates interaction with the data.

We describe the Outer Rim cosmological simulation, one of the largest high-resolution N-body simulations performed to date, aimed at promoting science to be carried out with large-scale structure surveys. The simulation covers a volume of (4.225Gpc) 3 and evolves more than one trillion particles. It was executed on Mira, a Blue-Gene/Q system at the Argonne Leadership Computing Facility. We discuss some of the computational challenges posed by a system like Mira, a many-core supercomputer, and how the simulation code, HACC, has been designed to overcome these challenges. We have carried out a large range of analyses on the simulation data and we report on the results as well as the data products that have been generated. The full data set generated by the simulation totals more than 5PB of data, making data curation and data handling a large challenge in of itself. The simulation results have been used to generate synthetic catalogs for large-scale structure surveys, including DESI and eBOSS, as well as CMB experiments. A detailed catalog for the LSST DESC data challenges has been created as well. We publicly release some of the Outer Rim halo catalogs, downsampled particle information, and lightcone data. Subject headings: methods: N-body -cosmology: large-scale structure of the universe

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 catalogues, 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 catalogue with galaxies drawn from the baseline library; by using weighted sampling guided by empirical modelling 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 catalogue 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, the Dark Energy Spectroscopic Instrument, WFIRST, and Euclid.