Preliminary Target Selection for the DESI Bright Galaxy Survey (BGS)
Omar Ruiz-Macias, Pauline Zarrouk, Shaun Cole, Peder Norberg, Carlton, Baugh, David Brooks, Arjun Dey, Yutong Duan, Sarah Eftekharzadeh, Daniel J., Eisenstein, Jaime E. Forero-Romero, Enrique Gazta\~naga, ChangHoon Hahn,, Robert Kehoe, Martin Landriau, Dustin Lang

TL;DR
This paper discusses the target selection process for the DESI Bright Galaxy Survey, focusing on star-galaxy separation, masking, and resulting galaxy densities to optimize low-redshift galaxy observations for cosmological measurements.
Contribution
It introduces the specific target selection criteria and procedures for the DESI BGS, including star-galaxy separation and masking strategies, to improve survey accuracy.
Findings
Target densities of ~800 objects/deg^2 for BRIGHT and ~600 objects/deg^2 for FAINT.
Implementation of star-galaxy separation and masking reduces spurious targets.
Selection process enhances the precision of low-redshift galaxy clustering measurements.
Abstract
The Dark Energy Spectroscopic Instrument (DESI) will execute a nearly magnitude-limited survey of low redshift galaxies (, median ). Clustering analyses of this Bright Galaxy Survey (BGS) will yield the most precise measurements to date of baryon acoustic oscillations and redshift-space distortions at low redshift. DESI BGS will comprise two target classes: (i) BRIGHT (~mag), and (ii) FAINT (~mag). Here we present a summary of the star-galaxy separation, and different photometric and geometrical masks, used in BGS to reduce the number of spurious targets. The selection results in a total density of objects/deg for the BRIGHT and objects/deg for the FAINT selections.A full characterization of the BGS selection can be found in Ruiz-Macias et al. (2020).
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
