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Characterizing the target selection pipeline for the Dark Energy Spectroscopic Instrument Bright Galaxy Survey

Ruiz-Macias, Omar and Zarrouk, Pauline and Cole, Shaun and Baugh, Carlton M and Norberg, Peder and Lucey, John and Dey, Arjun and Eisenstein, Daniel J and Doel, Peter and Gaztañaga, Enrique and Hahn, ChangHoon and Kehoe, Robert and Kitanidis, Ellie and Landriau, Martin and Lang, Dustin and Moustakas, John and Myers, Adam D and Prada, Francisco and Schubnell, Michael and Weinberg, David H and Wilson, M J (2021) 'Characterizing the target selection pipeline for the Dark Energy Spectroscopic Instrument Bright Galaxy Survey.', Monthly Notices of the Royal Astronomical Society., 502 (3). pp. 4328-4349.


We present the steps taken to produce a reliable and complete input galaxy catalogue for the Dark Energy Spectroscopic Instrument (DESI) Bright Galaxy Survey (BGS) using the photometric Legacy Survey DR8 DECam. We analyse some of the main issues faced in the selection of targets for the DESI BGS, such as star–galaxy separation, contamination by fragmented stars and bright galaxies. Our pipeline utilizes a new way to select BGS galaxies using Gaia photometry and we implement geometrical and photometric masks that reduce the number of spurious objects. The resulting catalogue is cross-matched with the Galaxy And Mass Assembly (GAMA) survey to assess the completeness of the galaxy catalogue and the performance of the target selection. We also validate the clustering of the sources in our BGS catalogue by comparing with mock catalogues and the Sloan Digital Sky Survey (SDSS) data. Finally, the robustness of the BGS selection criteria is assessed by quantifying the dependence of the target galaxy density on imaging and other properties. The largest systematic correlation we find is a 7 per cent suppression of the target density in regions of high stellar density.

Item Type:Article
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Publisher statement:2021 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Date accepted:26 January 2021
Date deposited:08 April 2021
Date of first online publication:03 February 2021
Date first made open access:08 April 2021

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