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1. A Southern Photometric Quasar Catalog from the Dark Energy Survey Data Release 2

   https://doi.org/10.3847/1538-4365/ac9ea8  

   Yang, Qian; Shen, Yue (December 2022, The Astrophysical Journal Supplement Series)

   Abstract We present a catalog of 1.4 million photometrically selected quasar candidates in the southern hemisphere over the ∼5000 deg²Dark Energy Survey (DES) wide survey area. We combine optical photometry from the DES second data release (DR2) with available near-infrared (NIR) and the all-sky unWISE mid-infrared photometry in the selection. We build models of quasars, galaxies, and stars with multivariate skew-tdistributions in the multidimensional space of relative fluxes as functions of redshift (or color for stars) and magnitude. Our selection algorithm assigns probabilities for quasars, galaxies, and stars and simultaneously calculates photometric redshifts (photo-z) for quasar and galaxy candidates. Benchmarking on spectroscopically confirmed objects, we successfully classify (with photometry) 94.7% of quasars, 99.3% of galaxies, and 96.3% of stars when all IR bands (NIRYJHKand WISE W1W2) are available. The classification and photo-zregression success rates decrease when fewer bands are available. Our quasar (galaxy) photo-zquality, defined as the fraction of objects with the difference between the photo-z z_pand the spectroscopic redshiftz_s, ∣Δz∣ ≡ ∣z_s−z_p∣/(1 +z_s) ≤ 0.1, is 92.2% (98.1%) when all IR bands are available, decreasing to 72.2% (90.0%) using optical DES data only. Our photometric quasar catalog achieves an estimated completeness of 89% and purity of 79% atr< 21.5 (0.68 million quasar candidates), with reduced completeness and purity at 21.5 <r≲ 24. Among the 1.4 million quasar candidates, 87,857 have existing spectra, and 84,978 (96.7%) of them are spectroscopically confirmed quasars. Finally, we provide quasar, galaxy, and star probabilities for all (0.69 billion) photometric sources in the DES DR2 coadded photometric catalog. 

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2. The DESI Bright Galaxy Survey: Final Target Selection, Design, and Validation

   https://doi.org/10.3847/1538-3881/accff8  

   Hahn, ChangHoon; Wilson, Michael_J; Ruiz-Macias, Omar; Cole, Shaun; Weinberg, David_H; Moustakas, John; Kremin, Anthony; Tinker, Jeremy_L; Smith, Alex; Wechsler, Risa_H; et al (May 2023, The Astronomical Journal)

   Abstract 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. 

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3. The Galaxy–Halo Connection of DESI Luminous Red Galaxies with Subhalo Abundance Matching

   https://doi.org/10.3847/1538-4357/ace76e  

   Berti, Angela M; Dawson, Kyle S; Dominguez, Wilber (August 2023, The Astrophysical Journal)

   Abstract We use subhalo abundance and age distribution matching to create magnitude-limited mock galaxy catalogs atz∼ 0.43, 0.52, and 0.63 withz-band and 3.4μmW1-band absolute magnitudes andr−zandr−W1 colors. From these magnitude-limited mocks, we select mock luminous red galaxy (LRG) samples according to the (r−z)-based (optical) and (r−W1)-based (infrared) selection criteria for the LRG sample of the Dark Energy Spectroscopic Instrument (DESI) survey. Our models reproduce the number densities, luminosity functions, color distributions, and projected clustering of the DESI Legacy Surveys that are the basis for DESI LRG target selection. We predict the halo occupation statistics of both optical and IR DESI LRGs at fixed cosmology and assess the differences between the two LRG samples. We find that IR-based SHAM modeling represents the differences between the optical and IR LRG populations better than using thezband and that age distribution matching overpredicts the clustering of LRGs, implying that galaxy color is uncorrelated with halo age in the LRG regime. Both the optical and IR DESI LRG target selections exclude some of the most luminous galaxies that would appear to be LRGs based on their position on the red sequence in optical color–magnitude space. Both selections also yield populations with a nontrivial LRG–halo connection that does not reach unity for the most massive halos. We find that the IR selection achieves greater completeness (≳90%) than the optical selection across all redshift bins studied. 

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4. A composite likelihood approach for inference under photometric redshift uncertainty

   https://doi.org/10.1093/mnras/stab3290  

   Rau, M. M.; Morrison, C. B.; Schmidt, S. J.; Wilson, S.; Mandelbaum, R.; Mao, Y-Y; LSST Dark Energy Science Collaboration; Alonso, David; Hartley, Will; Kirkby, David; et al (November 2021, Monthly Notices of the Royal Astronomical Society)

   ABSTRACT Obtaining accurately calibrated redshift distributions of photometric samples is one of the great challenges in photometric surveys like LSST, Euclid, HSC, KiDS, and DES. We present an inference methodology that combines the redshift information from the galaxy photometry with constraints from two-point functions, utilizing cross-correlations with spatially overlapping spectroscopic samples, and illustrate the approach on CosmoDC2 simulations. Our likelihood framework is designed to integrate directly into a typical large-scale structure and weak lensing analysis based on two-point functions. We discuss efficient and accurate inference techniques that allow us to scale the method to the large samples of galaxies to be expected in LSST. We consider statistical challenges like the parametrization of redshift systematics, discuss and evaluate techniques to regularize the sample redshift distributions, and investigate techniques that can help to detect and calibrate sources of systematic error using posterior predictive checks. We evaluate and forecast photometric redshift performance using data from the CosmoDC2 simulations, within which we mimic a DESI-like spectroscopic calibration sample for cross-correlations. Using a combination of spatial cross-correlations and photometry, we show that we can provide calibration of the mean of the sample redshift distribution to an accuracy of at least 0.002(1 + z), consistent with the LSST-Y1 science requirements for weak lensing and large-scale structure probes. 

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5. The Sensitivity of GPz Estimates of Photo-z Posterior PDFs to Realistically Complex Training Set Imperfections

   https://doi.org/10.1088/1538-3873/ac59bf  

   Stylianou, Natalia; Malz, Alex I.; Hatfield, Peter; Crenshaw, John Franklin; Gschwend, Julia (April 2022, Publications of the Astronomical Society of the Pacific)

   Abstract The accurate estimation of photometric redshifts is crucial to many upcoming galaxy surveys, for example, the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). Almost all Rubin extragalactic and cosmological science requires accurate and precise calculation of photometric redshifts; many diverse approaches to this problem are currently in the process of being developed, validated, and tested. In this work, we use the photometric redshift code GPz to examine two realistically complex training set imperfections scenarios for machine learning based photometric redshift calculation: (i) where the spectroscopic training set has a very different distribution in color–magnitude space to the test set, and (ii) where the effect of emission line confusion causes a fraction of the training spectroscopic sample to not have the true redshift. By evaluating the sensitivity of GPz to a range of increasingly severe imperfections, with a range of metrics (both of photo- z point estimates as well as posterior probability distribution functions, PDFs), we quantify the degree to which predictions get worse with higher degrees of degradation. In particular, we find that there is a substantial drop-off in photo- z quality when line-confusion goes above ∼1%, and sample incompleteness below a redshift of 1.5, for an experimental setup using data from the Buzzard Flock synthetic sky catalogs. 

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