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

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

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.

Cosmology: Observations, Galaxies: Distances and redshifts
0035-8711
1664-1682
Gatti, M.
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D'Andrea, C.B.
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Smith, M.
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The DES Collaboration
Gatti, M.
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Smith, M.
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Smith, M. , The DES Collaboration (2018) Dark Energy Survey Year 1 results: Cross-correlation redshifts - methods and systematics characterization. Monthly Notices of the Royal Astronomical Society, 477 (2), 1664-1682. (doi:10.1093/mnras/sty466).

Record type: Article

Abstract

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.

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More information

e-pub ahead of print date: 22 February 2018
Published date: 21 June 2018
Keywords: Cosmology: Observations, Galaxies: Distances and redshifts
Learn more about Physics & Astronomy research

Identifiers

Local EPrints ID: 423361
URI: http://eprints.soton.ac.uk/id/eprint/423361
DOI: doi:10.1093/mnras/sty466
ISSN: 0035-8711
PURE UUID: 0097828e-bcd8-4b43-b2e8-cd8e66a83ad6
ORCID for M. Smith: ORCID iD orcid.org/0000-0002-3321-1432

Catalogue record

Date deposited: 20 Sep 2018 16:30
Last modified: 26 Nov 2021 03:03

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Contributors

Author: M. Gatti
Author: P. Vielzeuf
Author: R. Cawthon
Author: M.M. Rau
Author: J. DeRose
Author: J. De Vicente
Author: A. Alarcon
Author: E. Rozo
Author: E. Gaztanaga
Author: B. Hoyle
Author: R. Miquel
Author: G. M. Bernstein
Author: C. Bonnett
Author: A. Carnero Rosell
Author: F.J. Castander
Author: C. Chang
Author: L.N. da Costa
Author: D. Gruen
Author: J. Gschwend
Author: W.G. Hartley
Author: H. Lin
Author: N. MacCrann
Author: M.A.G. Maia
Author: R.L.C. Ogando
Author: A. Roodman
Author: I. Sevilla-Noarbe
Author: M. A. Troxel
Author: R.H. Wechsler
Author: J. Asorey
Author: T.M. Davis
Author: K. Glazebrook
Author: S.R. Hinton
Author: G. Lewis
Author: C. Lidman
Author: E. Macaulay
Author: A. Möller
Author: C.R. O'Neill
Author: N.E. Sommer
Author: S.A. Uddin
Author: F. Yuan
Author: B. Zhang
Author: T.M.C. Abbott
Author: S. Allam
Author: J. Annis
Author: K. Bechtol
Author: D. Brooks
Author: D.L. Burke
Author: D. Carollo
Author: C.B. D'Andrea
Author: M. Smith ORCID iD
Corporate Author: The DES Collaboration

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