The LSST DESC data challenge 1: Generation and analysis of synthetic images for next-generation surveys

Journal Article

© 2020 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. Data Challenge 1 (DC1) is the first synthetic data set produced by the Rubin Observatory Legacy Survey of Space and Time (LSST) Dark Energy Science Collaboration (DESC). DC1 is designed to develop and validate data reduction and analysis and to study the impact of systematic effects that will affect the LSST data set. DC1 is comprised of r-band observations of 40 deg2 to 10 yr 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 catalogue for validation and testing. We verify that testable LSST requirements pass within the fidelity of DC1. We establish a selection procedure that produces a sufficiently clean extragalactic sample for clustering analyses 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 and conclude that: (i) survey properties have an impact of 50 per cent of the statistical uncertainty for the scales and models used in DC1; (ii) a selection to eliminate artefacts in the catalogues is necessary to avoid biases in the measured clustering; and (iii) the presence of bright objects has a significant impact (2-6) in the estimated power spectra at small scales (> 1200), highlighting the impact of blending in studies at small angular scales in LSST.

Full Text

Duke Authors

Cited Authors

  • Sanchez, J; Walter, CW; Awan, H; Chiang, J; Daniel, SF; Gawiser, E; Glanzman, T; Kirkby, D; Mandelbaum, R; Slosar, A; Wood-Vasey, WM; Alsayyad, Y; Burke, CJ; Digel, SW; Jarvis, M; Johnson, T; Kelly, H; Krughoff, S; Lupton, RH; Marshall, PJ; Peterson, JR; Price, PA; Sembroski, G; Van Klaveren, B; Wiesner, MP; Xin, B

Published Date

  • September 1, 2020

Published In

Volume / Issue

  • 497 / 1

Start / End Page

  • 210 - 228

Electronic International Standard Serial Number (EISSN)

  • 1365-2966

International Standard Serial Number (ISSN)

  • 0035-8711

Digital Object Identifier (DOI)

  • 10.1093/mnras/staa1957

Citation Source

  • Scopus