Skip to content

Dark Energy Survey Year 1 results: cross-correlation redshifts – methods and systematics characterization

Research output: Contribution to journalArticle

  • M. Gatti
  • P. Vielzeuf
  • C. Davis
  • R. Cawthon
  • M. M. Rau
  • J. DeRose
  • J. De Vicente
  • A. Alarcon
  • E. Rozo
  • E. Gaztanaga
  • B. Hoyle
  • R. Miquel
  • G. M. Bernstein
  • C. Bonnett
  • A. Carnero Rosell
  • F. J. Castander
  • C. Chang
  • L. N. Da Costa
  • D. Gruen
  • J. Gschwend
  • W. G. Hartley
  • H. Lin
  • N. MacCrann
  • M. A. G. Maia
  • R. L. C. Ogando
  • A. Roodman
  • I. Sevilla-Noarbe
  • M. A. Troxel
  • R. H. Wechsler
  • J. Asorey
  • T. M. Davis
  • K. Glazebrook
  • S. R. Hinton
  • G. Lewis
  • C. Lidman
  • E. Macaulay
  • A. Möller
  • C. R. O'Neill
  • N. E. Sommer
  • S. A. Uddin
  • F. Yuan
  • B. Zhang
  • T. M. C. Abbott
  • S. Allam
  • J. Annis
  • K. Bechtol
  • D. Brooks
  • C. B. D'Andrea
  • D. L. DePoy
  • S. Desai
  • T. F. Eifler
  • A. E. Evrard
  • B. Flaugher
  • P. Fosalba
  • J. Frieman
  • J. Garcıa-Bellido
  • D. W. Gerdes
  • D. A. Goldstein
  • R. A. Gruendl
  • G. Gutierrez
  • K. Honscheid
  • J. K. Hoormann
  • B. Jain
  • D. J. James
  • M. Jarvis
  • T. Jeltema
  • M. W. G. Johnson
  • M. D. Johnson
  • E. Krause
  • K. Kuehn
  • S. Kuhlmann
  • N. Kuropatkin
  • T. S. Li
  • M. Lima
  • J. L. Marshall
  • P. Melchior
  • F. Menanteau
  • B. Nord
  • A. A. Plazas
  • K. Reil
  • E. S. Rykoff
  • M. Sako
  • E. Sanchez
  • V. Scarpine
  • M. Schubnell
  • E. Sheldon
  • M. Smith
  • R. C. Smith
  • M. Soares-Santos
  • F. Sobreira
  • E. Suchyta
  • M. E. C. Swanson
  • G. Tarle
  • B. E. Tucker
  • D. L. Tucker
  • V. Vikram
  • A. R. Walker
  • J. Weller
  • W. Wester
  • R. C. Wolf
We use numerical simulations to characterize the performance of a clustering-based method to calibrate photometric redshift biases. In particular, we cross-correlate the weak lensing source galaxies from the Dark Energy Survey Year 1 sample with redMaGiC galaxies (luminous red galaxies with secure photometric redshifts) to estimate the redshift distribution of the former sample. The recovered redshift distributions are used to calibrate the photometric redshift bias of standard photo-z methods applied to the same source galaxy sample. We apply the method to two photo-z codes run in our simulated data: Bayesian Photometric Redshift and Directional Neighbourhood Fitting. We characterize the systematic uncertainties of our calibration procedure, and find that these systematic uncertainties dominate our error budget. The dominant systematics are due to our assumption of unevolving bias and clustering across each redshift bin, and to differences between the shapes of the redshift distributions derived by clustering versus photo-zs. The systematic uncertainty in the mean redshift bias of the source galaxy sample is Δz ≲ 0.02, though the precise value depends on the redshift bin under consideration. We discuss possible ways to mitigate the impact of our dominant systematics in future analyses.
Original languageEnglish
Pages (from-to)1664-1682
JournalMonthly Notices of the Royal Astronomical Society
Volume477
Issue number2
Early online date22 Feb 2018
DOIs
Publication statusPublished - Jun 2018

Documents

  • Dark Energy Survey Year 1 results

    Rights statement: This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society ©: 2018 The Authors. Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.

    Final published version, 1.31 MB, PDF document

Relations Get citation (various referencing formats)

ID: 10455679