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Dark energy survey year 1 results: Redshift distributions of the weak-lensing source galaxies

Dark energy survey year 1 results: Redshift distributions of the weak-lensing source galaxies
Dark energy survey year 1 results: Redshift distributions of the weak-lensing source galaxies

We describe the derivation and validation of redshift distribution estimates and their uncertainties for the populations of galaxies used as weak-lensing sources in the Dark Energy Survey (DES) Year 1 cosmological analyses. The Bayesian Photometric Redshift (BPZ) code is used to assign galaxies to four redshift bins between z ≈ 0.2 and ≈1.3, and to produce initial estimates of the lensing-weighted redshift distributions nPZ i(z) ∝ dni/dz for members of bin i. Accurate determination of cosmological parameters depends critically on knowledge of ni, but is insensitive to bin assignments or redshift errors for individual galaxies. The cosmological analyses allow for shifts ni (z) = nPZ i(z - Δzi) to correct themean redshift of ni(z) for biases in nPZ i. The Δzi are constrained by comparison of independently estimated 30-band photometric redshifts of galaxies in the Cosmic Evolution Survey (COSMOS) field to BPZ estimates made from the DES griz fluxes, for a sample matched in fluxes, pre-seeing size, and lensing weight to the DES weak-lensing sources. In companion papers, the Δzi of the three lowest redshift bins are further constrained by the angular clustering of the source galaxies around red galaxies with secure photometric redshifts at 0.15 < z < 0.9. This paper details the BPZ and COSMOS procedures, and demonstrates that the cosmological inference is insensitive to details of the ni(z) beyond the choice of Δzi. The clustering and COSMOS validation methods produce consistent estimates of Δzi in the bins where both can be applied, with combined uncertainties of σΔzi = 0.015, 0.013, 0.011, and 0.022 in the four bins. Repeating the photo-z procedure instead using the Directional Neighbourhood Fitting algorithm, or using the ni(z) estimated from the matched sample in COSMOS, yields no discernible difference in cosmological inferences.

Catalogues, Methods: Data analysis, Surveys
1365-2966
592-610
Hoyle, B.
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Gruen, D.
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Bernstein, G. M.
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Lewis, G.
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Lidman, C.
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Lin, H.
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Macaulay, E.
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Mudd, D.
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Möller, A.
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Nichol, R. C.
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D'Andrea, C. B.
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Smith, M.
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Thomas, D.
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DES Collaboration
Hoyle, B.
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Gruen, D.
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De Vicente, J.
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Sánchez, C.
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Wechsler, R. H.
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Childress, M.
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Gschwend, J.
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Lewis, G.
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Lidman, C.
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Lin, H.
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Macaulay, E.
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Maia, M. A.G.
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Mudd, D.
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Nichol, R. C.
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Smith, M.
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Thomas, D.
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Smith, M. , DES Collaboration (2018) Dark energy survey year 1 results: Redshift distributions of the weak-lensing source galaxies. Monthly Notices of the Royal Astronomical Society, 478 (1), 592-610. (doi:10.1093/mnras/sty957).

Record type: Article

Abstract

We describe the derivation and validation of redshift distribution estimates and their uncertainties for the populations of galaxies used as weak-lensing sources in the Dark Energy Survey (DES) Year 1 cosmological analyses. The Bayesian Photometric Redshift (BPZ) code is used to assign galaxies to four redshift bins between z ≈ 0.2 and ≈1.3, and to produce initial estimates of the lensing-weighted redshift distributions nPZ i(z) ∝ dni/dz for members of bin i. Accurate determination of cosmological parameters depends critically on knowledge of ni, but is insensitive to bin assignments or redshift errors for individual galaxies. The cosmological analyses allow for shifts ni (z) = nPZ i(z - Δzi) to correct themean redshift of ni(z) for biases in nPZ i. The Δzi are constrained by comparison of independently estimated 30-band photometric redshifts of galaxies in the Cosmic Evolution Survey (COSMOS) field to BPZ estimates made from the DES griz fluxes, for a sample matched in fluxes, pre-seeing size, and lensing weight to the DES weak-lensing sources. In companion papers, the Δzi of the three lowest redshift bins are further constrained by the angular clustering of the source galaxies around red galaxies with secure photometric redshifts at 0.15 < z < 0.9. This paper details the BPZ and COSMOS procedures, and demonstrates that the cosmological inference is insensitive to details of the ni(z) beyond the choice of Δzi. The clustering and COSMOS validation methods produce consistent estimates of Δzi in the bins where both can be applied, with combined uncertainties of σΔzi = 0.015, 0.013, 0.011, and 0.022 in the four bins. Repeating the photo-z procedure instead using the Directional Neighbourhood Fitting algorithm, or using the ni(z) estimated from the matched sample in COSMOS, yields no discernible difference in cosmological inferences.

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More information

e-pub ahead of print date: 18 April 2018
Published date: 21 July 2018
Keywords: Catalogues, Methods: Data analysis, Surveys

Identifiers

Local EPrints ID: 425608
URI: http://eprints.soton.ac.uk/id/eprint/425608
ISSN: 1365-2966
PURE UUID: f90d4bb4-ded8-4f08-98b4-9fbbe8c632a0
ORCID for M. Smith: ORCID iD orcid.org/0000-0002-3321-1432

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Date deposited: 25 Oct 2018 16:30
Last modified: 16 Mar 2024 04:19

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Contributors

Author: B. Hoyle
Author: D. Gruen
Author: G. M. Bernstein
Author: M. M. Rau
Author: J. De Vicente
Author: W. G. Hartley
Author: E. Gaztanaga
Author: J. DeRose
Author: M. A. Troxel
Author: C. Davis
Author: A. Alarcon
Author: N. MacCrann
Author: J. Prat
Author: C. Sánchez
Author: E. Sheldon
Author: R. H. Wechsler
Author: J. Asorey
Author: M. R. Becker
Author: C. Bonnett
Author: A. Carnero Rosell
Author: D. Carollo
Author: M. Carrasco Kind
Author: F. J. Castander
Author: R. Cawthon
Author: C. Chang
Author: M. Childress
Author: T. M. Davis
Author: A. Drlica-Wagner
Author: M. Gatti
Author: K. Glazebrook
Author: J. Gschwend
Author: S. R. Hinton
Author: J. K. Hoormann
Author: A. G. Kim
Author: A. King
Author: K. Kuehn
Author: G. Lewis
Author: C. Lidman
Author: H. Lin
Author: E. Macaulay
Author: M. A.G. Maia
Author: P. Martini
Author: D. Mudd
Author: A. Möller
Author: R. C. Nichol
Author: R. L.C. Ogando
Author: R. P. Rollins
Author: C. B. D'Andrea
Author: M. Smith ORCID iD
Author: D. Thomas
Corporate Author: DES Collaboration

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