Preliminary Target Selection for the DESI Bright Galaxy Survey (BGS)

, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , and

Published October 2020 © 2020. The American Astronomical Society. All rights reserved.
, , Citation Omar Ruiz-Macias et al 2020 Res. Notes AAS 4 187 DOI 10.3847/2515-5172/abc25a

2515-5172/4/10/187

Abstract

The Dark Energy Spectroscopic Instrument (DESI) will execute a nearly magnitude-limited survey of low redshift galaxies (0.05 ≤ z ≤ 0.4, median z ≈ 0.2). 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 (r < 19.5 mag), and (ii) FAINT (19.5 < r < 20 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 ∼800 objects deg−2 for the BRIGHT and ∼600 objects deg−2 for the FAINT selections. A full characterization of the BGS selection can be found in Ruiz-Macias et al.

Export citation and abstract BibTeX RIS

1. Introduction

The Dark Energy Spectroscopic Instrument (DESI) Bright Galaxy Survey (BGS) (DESI Collaboration et al. 2016) will be a flux-limited r-band selected sample of 10 million galaxies to z = 0.4. The DESI target selection will be done on the Legacy Surveys (LS) imaging (Dey et al. 2019). Results presented here are based on the DR8 release. 26 The DESI BGS is expected to have a target density of about 800 galaxies deg−2 in a primary selection with a magnitude limit in the r-band of 19.5 and 600 galaxies deg−2 in an additional sample defined by the magnitude range 19.5 < r < 20 (Smith et al. 2017). Henceforth, we will refer to these BGS samples as BRIGHT and FAINT respectively.

2. BGS Target Selection

The BGS targets are selected using the three optical fluxes in the Legacy Surveys (Ruiz-Macias et al. 2020):

Equation (1)

Equation (2)

Equation (3)

Equation (4)

where g, r, and z indicate the extinction-corrected AB magnitudes in the corresponding band (using LS extinction corrections). In addition, we require all targets to be covered by at least one image in each optical band. We remove sources near bright stars, large galaxies, or globular clusters by requiring that LS MASKBITS 1, 12 and 13 are not set as defined in the LS data model. The bright star mask (defined by bit = 1) combines stars from Gaia DR2 (Gaia Collaboration et al. 2018) and the Tycho2 (Høg et al. 2000) catalog, corrected for epoch and proper motions. This mask consists of a circular exclusion region with a radius that depends on the magnitude of the star, m. The magnitude is either the Tycho2 mag_vt or Gaia G-mag with Gaia G-mag taking precedence. Stars fainter than m = 13 are not masked. The large galaxies mask (bit = 12) was defined by the Siena Galaxy Atlas—2020 (J. Moustakas 2020, in preparation), an angular diameter-limited sample of galaxies with mostly HyperLeda objects (Makarov et al. 2014). To mask around large galaxies and elliptical mask is used defined by the diameter at the 25 mag arcsec−2 (optical) surface brightness, the ratio of the semiminor axis to the to semimajor axis and the position angle. The globular cluster (GC) mask (bit = 13) consists of a circular exclusion zone around known GCs from the OpenNGC catalog. 27

BGS star-galaxy separation is based on Gaia DR2. Gaia is highly complete for BGS and has a better point-spread function than ground-based surveys. A galaxy in BGS is defined by (G − rr > 0.6) or  (G = 0) where G is Gaia G-mag and rr is LS r-band magnitude (without any extinction correction). We apply Equations (2) and (3) to remove sources that are beyond the color range of BGS galaxies. To remove spurious large and low-surface-brightness galaxies, we implement a cut that compares the r-band magnitude (r) to the predicted r-band fiber magnitude (rfibmag), as in Equation (4). Finally, we impose a minimum quality for the data reduced by The Tractor 28 (Lang et al. 2016), in FRACMASKED_i < 0.4, FRACIN_i > 0.3, FRACFLUX_i < 5, where i ≡ g, r or z. FRACIN is used to select sources for which a large fraction of the model flux lies within the contiguous pixels to which the model was fitted, FRACFLUX is used to reject objects that are swamped by flux from adjacent sources, and FRACMASKED is used to veto objects with a high fraction of masked pixels.

3. Conclusion

We have presented the BGS target selection from LS DR8 divided into two samples, the BRIGHT and FAINT samples. The BRIGHT sample, which will have a higher fiber-allocation priority, comprises ∼800 targets deg−2. The FAINT sample, which will be assigned at lower priority, comprises ∼600 objects deg−2. Both samples undergo the spatial and photometric cuts outlined by the MASKBITS and the number of observations required, and by Equations (2)–(4) respectively, passed the Gaia based star-galaxy classification and our quality cuts. Figure 1 shows the BGS target density as a function of r-band magnitude and the N(z) of BGS targets cross-matched to GAMA DR4 (Driver et al. 2012; Liske et al. 2015; Baldry et al. 2017). The preliminary selection described in this note is public. 29

Figure 1.

Figure 1. Left: target density of BGS as a function of r-band magnitude in DECaLS DR8. The red dashed line at 19.5 marks the boundary between the BRIGHT and FAINT selections. Right: redshift distribution of DECaLS DR8 BGS targets cross-matched with GAMA DR4 in redshift bins of Δz = 0.02. The solid blue, orange and gray lines show the BRIGHT, FAINT and combined samples respectively. The dashed lines show the means of the samples at 0.21 (BRIGHT), 0.26 (FAINT) and 0.22 (combined). Error bars for BRIGHT and FAINT distributions are Poisson. Because GAMA has a magnitude limit of approximately r = 19.8 in (SDSS DR7) r-petrosian (Abazajian et al. 2009; Driver et al. 2012), only 30% of the FAINT sample appears in GAMA and the mean redshift is expected to be somewhat higher. While for the BRIGHT sample, the GAMA completeness is as high as 95%.

Standard image High-resolution image

O.R.-M. is supported by the Mexican National Council of Science and Technology (CONACyT) through grant No. 297228/440775 and funding from the European Union's Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie grant agreement No. 734374. This research is supported by the Director, Office of Science, Office of High Energy Physics of the U.S. Department of Energy under Contract No. DE-AC02-05CH1123, and by the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility under the same contract; additional support for DESI is provided by the U.S. National Science Foundation, Division of Astronomical Sciences under Contract No. AST-0950945 to the NSF's National Optical-Infrared Astronomy Research Laboratory; the Science and Technologies Facilities Council of the United Kingdom; the Gordon and Betty Moore Foundation; the Heising-Simons Foundation; the French Alternative Energies and Atomic Energy Commission (CEA); the National Council of Science and Technology of Mexico; the Ministry of Economy of Spain, and by the DESI Member Institutions. The authors are honored to be permitted to conduct astronomical research on Iolkam Du'ag (Kitt Peak), a mountain with particular significance to the Tohono O'odham Nation.

Footnotes

Please wait… references are loading.
10.3847/2515-5172/abc25a