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We explore the galaxy-halo connection information that is available in low-redshift samples from the early data release of the Dark Energy Spectroscopic Instrument (DESI). We model the halo occupation distribution (HOD) fromz= 0.1 to 0.3 using Survey Validation 3 (SV3; a.k.a., the One-Percent Survey) data of the DESI Bright Galaxy Survey. In addition to more commonly used metrics, we incorporate counts-in-cylinders (CiC) measurements, which drastically tighten HOD constraints. Our analysis is aided by the Python package,galtab, which enables the rapid, precise prediction of CiC for any HOD model available inhalotools. This methodology allows our Markov chains to converge with much fewer trial points, and enables even more drastic speedups due to its GPU portability. Our HOD fits constrain characteristic halo masses tightly and provide statistical evidence for assembly bias, especially at lower luminosity thresholds: the HOD of central galaxies inz∼ 0.15 samples with limiting absolute magnitudeM_r< −20.0 andM_r< −20.5 samples is positively correlated with halo concentration with a significance of 99.9% and 99.5%, respectively. Our models also favor positive central assembly bias for the brighterM_r< −21.0 sample atz∼ 0.25 (94.8% significance), but there is no significant evidence for assembly bias with the same luminosity threshold atz∼ 0.15. We provide our constraints for each threshold sample’s characteristic halo masses, assembly bias, and other HOD parameters. These constraints are expected to be significantly tightened with future DESI data, which will span an area 100 times larger than that of SV3.

 

Analysis of large galaxy surveys requires confidence in the robustness of numerical simulation methods. The simulations are used to construct mock galaxy catalogues to validate data analysis pipelines and identify potential systematics. We compare three N-body simulation codes, abacus, gadget-2, and swift, to investigate the regimes in which their results agree. We run N-body simulations at three different mass resolutions, 6.25 × 108, 2.11 × 109, and 5.00 × 109 h−1 M⊙, matching phases to reduce the noise within the comparisons. We find systematic errors in the halo clustering between different codes are smaller than the Dark Energy Spectroscopic Instrument (DESI) statistical error for $s\ \gt\ 20\ h^{-1}$ Mpc in the correlation function in redshift space. Through the resolution comparison we find that simulations run with a mass resolution of 2.1 × 109 h−1 M⊙ are sufficiently converged for systematic effects in the halo clustering to be smaller than the DESI statistical error at scales larger than $20\ h^{-1}$ Mpc. These findings show that the simulations are robust for extracting cosmological information from large scales which is the key goal of the DESI survey. Comparing matter power spectra, we find the codes agree to within 1 per cent for k ≤ 10 h Mpc−1. We also run a comparison of three initial condition generation codes and find good agreement. In addition, we include a quasi-N-body code, FastPM, since we plan use it for certain DESI analyses. The impact of the halo definition and galaxy–halo relation will be presented in a follow-up study.

 

Over the next 5 yr, the Dark Energy Spectroscopic Instrument (DESI) will use 10 spectrographs with 5000 fibers on the 4 m Mayall Telescope at Kitt Peak National Observatory to conduct the first Stage IV dark energy galaxy survey. Atz< 0.6, the DESI Bright Galaxy Survey (BGS) will produce the most detailed map of the universe during the dark-energy-dominated epoch with redshifts of >10 million galaxies spanning 14,000 deg². In this work, we present and validate the final BGS target selection and survey design. From the Legacy Surveys, BGS will target anr< 19.5 mag limited sample (BGS Bright), a fainter 19.5 <r< 20.175 color-selected sample (BGS Faint), and a smaller low-zquasar sample. BGS will observe these targets using exposure times scaled to achieve homogeneous completeness and cover the footprint three times. We use observations from the Survey Validation programs conducted prior to the main survey along with simulations to show that BGS can complete its strategy and make optimal use of “bright” time. BGS targets have stellar contamination <1%, and their densities do not depend strongly on imaging properties. BGS Bright will achieve >80% fiber assignment efficiency. Finally, BGS Bright and BGS Faint will achieve >95% redshift success over any observing condition. BGS meets the requirements for an extensive range of scientific applications. BGS will yield the most precise baryon acoustic oscillation and redshift-space distortion measurements atz< 0.4. It presents opportunities for new methods that require highly complete and dense samples (e.g.,N-point statistics, multitracers). BGS further provides a powerful tool to study galaxy populations and the relations between galaxies and dark matter.

 

ABSTRACT 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.