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2dFLenS and KiDS: determining source redshift distributions with cross-correlations

Johnson, A, Blake, C, Amon, A, Erben, T, Glazebrook, K, Harnois-Déraps, J, Heymans, C, Hildebrandt, H, Joudaki, S, Klaes, D, Kuijken, K, Lidman, C, Marin, FA, McFarland, J, Morrison, CB, Parkinson, D, Poole, GB, Radovich, M and Wolf, C (2016) 2dFLenS and KiDS: determining source redshift distributions with cross-correlations. Monthly Notices of the Royal Astronomical Society, 465 (4). pp. 4118-4132. ISSN 0035-8711

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We develop a statistical estimator to infer the redshift probability distribution of a photometric sample of galaxies from its angular cross-correlation in redshift bins with an overlapping spectroscopic sample. This estimator is a minimum-variance weighted quadratic function of the data: a quadratic estimator. This extends and modifies the methodology presented by McQuinn & White. The derived source redshift distribution is degenerate with the source galaxy bias, which must be constrained via additional assumptions. We apply this estimator to constrain source galaxy redshift distributions in the Kilo-Degree imaging survey through cross-correlation with the spectroscopic 2-degree Field Lensing Survey, presenting results first as a binned step-wise distribution in the range z < 0.8, and then building a continuous distribution using a Gaussian process model. We demonstrate the robustness of our methodology using mock catalogues constructed from N-body simulations, and comparisons with other techniques for inferring the redshift distribution.

Item Type: Article
Additional Information: This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society ©: 2016 The Authors Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved.
Uncontrolled Keywords: 0201 Astronomical and Space Sciences
Subjects: Q Science > QB Astronomy
Q Science > QC Physics
Divisions: Astrophysics Research Institute
Publisher: Oxford University Press (OUP)
Date Deposited: 23 Jul 2020 11:26
Last Modified: 04 Sep 2021 06:56
DOI or ID number: 10.1093/mnras/stw3033
URI: https://researchonline.ljmu.ac.uk/id/eprint/13355
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