Target Selection and Validation of DESI Luminous Red Galaxies

The Dark Energy Spectroscopic Instrument (DESI) is carrying out a five-year survey that aims to measure the redshifts of tens of millions of galaxies and quasars, including 8 million luminous red galaxies (LRGs) in the redshift range 0.4 < z ≲ 1.0. Here we present the selection of the DESI LRG sample and assess its spectroscopic performance using data from Survey Validation (SV) and the first two months of the Main Survey. The DESI LRG sample, selected using g, r, z, and W1 photometry from the DESI Legacy Imaging Surveys, is highly robust against imaging systematics. The sample has a target density of 605 deg−2 and a comoving number density of 5 × 10−4 h 3 Mpc−3 in 0.4 < z < 0.8; this is a significantly higher density than previous LRG surveys (such as SDSS, BOSS, and eBOSS) while also extending to z ∼ 1. After applying a bright star veto mask developed for the sample, 98.9% of the observed LRG targets yield confident redshifts (with a catastrophic failure rate of 0.2% in the confident redshifts), and only 0.5% of the LRG targets are stellar contamination. The LRG redshift efficiency varies with source brightness and effective exposure time, and we present a simple model that accurately characterizes this dependence. In the appendices, we describe the extended LRG samples observed during SV.

[1]  M. Levi,et al.  Intrinsic alignment as an RSD contaminant in the DESI survey , 2022, Monthly Notices of the Royal Astronomical Society.

[2]  Sergey E. Koposov,et al.  Overview of the Instrumentation for the Dark Energy Spectroscopic Instrument , 2022, The Astronomical Journal.

[3]  M. Levi,et al.  Cosmological constraints from the tomographic cross-correlation of DESI Luminous Red Galaxies and Planck CMB lensing , 2021, Journal of Cosmology and Astroparticle Physics.

[4]  P. J. Richards,et al.  Gaia Early Data Release 3: Summary of the contents and survey properties , 2020, 2012.01533.

[5]  A. Myers,et al.  Preliminary Target Selection for the DESI Luminous Red Galaxy (LRG) Sample , 2020, Research Notes of the AAS.

[6]  A. Myers,et al.  The Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: Large-scale structure catalogues for cosmological analysis , 2020, Monthly Notices of the Royal Astronomical Society.

[7]  Jaime Fern'andez del R'io,et al.  Array programming with NumPy , 2020, Nature.

[8]  A. Myers,et al.  The Clustering of DESI-like Luminous Red Galaxies Using Photometric Redshifts , 2020, Monthly Notices of the Royal Astronomical Society.

[9]  D. Lang,et al.  unWISE Coadds: The Five-year Data Set , 2019, Publications of the Astronomical Society of the Pacific.

[10]  Joel Nothman,et al.  SciPy 1.0-Fundamental Algorithms for Scientific Computing in Python , 2019, ArXiv.

[11]  Leo Singer,et al.  healpy: equal area pixelization and spherical harmonics transforms for data on the sphere in Python , 2019, J. Open Source Softw..

[12]  J. Kneib,et al.  Testing gravity with galaxy-galaxy lensing and redshift-space distortions using CFHT-Stripe 82, CFHTLenS, and BOSS CMASS datasets , 2019, Astronomy & Astrophysics.

[13]  A. Slosar,et al.  Cosmological constraints from galaxy–lensing cross-correlations using BOSS galaxies with SDSS and CMB lensing , 2018, Monthly Notices of the Royal Astronomical Society.

[14]  et al,et al.  Gaia Data Release 2 , 2018, Astronomy & Astrophysics.

[15]  Adam D. Myers,et al.  Overview of the DESI Legacy Imaging Surveys , 2018, The Astronomical Journal.

[16]  Miguel de Val-Borro,et al.  The Astropy Project: Building an Open-science Project and Status of the v2.0 Core Package , 2018, The Astronomical Journal.

[17]  A. Leauthaud,et al.  The Stripe 82 Massive Galaxy Project. III. A Lack of Growth among Massive Galaxies , 2017, 1711.10506.

[18]  Xiaohui Fan,et al.  Project Overview of the Beijing–Arizona Sky Survey , 2017, 1702.03653.

[19]  S. Ho,et al.  Testing gravity on large scales by combining weak lensing with galaxy clustering using CFHTLenS and BOSS CMASS , 2016, 1610.09410.

[20]  W. M. Wood-Vasey,et al.  The clustering of galaxies in the completed SDSS-III Baryon Oscillation Spectroscopic Survey: cosmological analysis of the DR12 galaxy sample , 2016, 1607.03155.

[21]  R. Nichol,et al.  The clustering of galaxies in the completed SDSS-III Baryon Oscillation Spectroscopic Survey: observational systematics and baryon acoustic oscillations in the correlation function , 2016, 1607.03145.

[22]  K. Andersen Figure 22 , 2016, Organic Photoreceptors for Imaging Systems.

[23]  David Schlegel,et al.  SDSS-III Baryon Oscillation Spectroscopic Survey Data Release 12: galaxy target selection and large-scale structure catalogues , 2015, 1509.06529.

[24]  A. Bolton,et al.  The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: modelling the clustering and halo occupation distribution of BOSS CMASS galaxies in the Final Data Release , 2015, 1509.06404.

[25]  R. Nichol,et al.  THE STRIPE 82 MASSIVE GALAXY PROJECT. I. CATALOG CONSTRUCTION , 2015, 1509.01276.

[26]  A. Myers,et al.  THE SDSS-IV EXTENDED BARYON OSCILLATION SPECTROSCOPIC SURVEY: LUMINOUS RED GALAXY TARGET SELECTION , 2015, 1508.04478.

[27]  R. Mandelbaum,et al.  Mapping stellar content to dark matter haloes using galaxy clustering and galaxy–galaxy lensing in the SDSS DR7 , 2015, 1505.02781.

[28]  Prasanth H. Nair,et al.  Astropy: A community Python package for astronomy , 2013, 1307.6212.

[29]  A. Slosar,et al.  Cosmological parameter constraints from galaxy-galaxy lensing and galaxy clustering with the SDSS DR7 , 2012, 1207.1120.

[30]  R. Nichol,et al.  The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: analysis of potential systematics , 2012, 1203.6499.

[31]  Martin G. Cohen,et al.  THE WIDE-FIELD INFRARED SURVEY EXPLORER (WISE): MISSION DESCRIPTION AND INITIAL ON-ORBIT PERFORMANCE , 2010, 1008.0031.

[32]  R. Nichol,et al.  Detection of the Baryon Acoustic Peak in the Large-Scale Correlation Function of SDSS Luminous Red Galaxies , 2005, astro-ph/0501171.

[33]  K. Gorski,et al.  HEALPix: A Framework for High-Resolution Discretization and Fast Analysis of Data Distributed on the Sphere , 2004, astro-ph/0409513.

[34]  Marcin Sawicki,et al.  The 1.6 Micron Bump as a Photometric Redshift Indicator , 2002, astro-ph/0209437.

[35]  V. Narayanan,et al.  Spectroscopic Target Selection for the Sloan Digital Sky Survey: The Luminous Red Galaxy Sample , 2001, astro-ph/0108153.

[36]  D. Schlegel,et al.  Maps of Dust IR Emission for Use in Estimation of Reddening and CMBR Foregrounds , 1997, astro-ph/9710327.

[37]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.