The LSST DESC Data Challenge 1: Generation and Analysis of Synthetic Images for Next Generation Surveys

Data Challenge 1 (DC1) is the first synthetic dataset produced by the Large Synoptic Survey Telescope (LSST) Dark Energy Science Collaboration (DESC). These are designed to develop and validate data reduction and analysis and to study the impact of systematic effects that will affect the LSST dataset. DC1 is comprised of $r$-band observations of 40 deg$^{2}$ to 10-year LSST depth. We present each stage of the simulation and analysis process: a) generation, by synthesizing sources from cosmological N-body simulations in individual sensor-visit images with different observing conditions; b) reduction using a development version of the LSST Science Pipelines; and c) matching to the input cosmological catalog for validation and testing. We study our processed catalogs compared to the LSST requirements key performance metrics (KPMs). We establish a set of pipeline flags that produce a sufficiently clean extragalactic sample and we discuss residual sample contamination, including contributions from inefficiency in star-galaxy separation and imperfect deblending. We compute the galaxy power spectrum on the simulated field. Our main conclusions are: i) realistic and validated synthetic datasets will be required for successfully controlling systematics; ii) within the fidelity of DC1, the LSST Science Pipelines pass all testable KPMs; iii) there is no unique noiseless method for matching the input and output catalogs; iv) the presence of bright objects has a significant impact (2- to 6-$\sigma$) in the estimated power spectra at small scales ($\ell > 1200$), highlighting the impact of blending in studies at small angular scales in LSST.