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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                            A Machine-learning Approach to Predict Missing Flux Densities in Multiband Galaxy Surveys
                        
                    
    
            Abstract We present a new method based on information theory to find the optimal number of bands required to measure the physical properties of galaxies with desired accuracy. As a proof of concept, using the recently updated COSMOS catalog (COSMOS2020), we identify the most relevant wave bands for measuring the physical properties of galaxies in a Hawaii Two-0- (H20) and UVISTA-like survey for a sample ofi< 25 AB mag galaxies. We find that with the availablei-band fluxes,r,u, IRAC/ch2, andzbands provide most of the information regarding the redshift with importance decreasing fromrband tozband. We also find that for the same sample, IRAC/ch2,Y,r, andubands are the most relevant bands in stellar-mass measurements with decreasing order of importance. Investigating the intercorrelation between the bands, we train a model to predict UVISTA observations in near-IR from H20-like observations. We find that magnitudes in theYJHbands can be simulated/predicted with an accuracy of 1σmag scatter ≲0.2 for galaxies brighter than 24 AB mag in near-IR bands. One should note that these conclusions depend on the selection criteria of the sample. For any new sample of galaxies with a different selection, these results should be remeasured. Our results suggest that in the presence of a limited number of bands, a machine-learning model trained over the population of observed galaxies with extensive spectral coverage outperforms template fitting. Such a machine-learning model maximally comprises the information acquired over available extensive surveys and breaks degeneracies in the parameter space of template fitting inevitable in the presence of a few bands. 
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                            - PAR ID:
- 10555785
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- Astrophysical Journal
- Date Published:
- Journal Name:
- The Astrophysical Journal
- Volume:
- 942
- Issue:
- 2
- ISSN:
- 0004-637X
- Page Range / eLocation ID:
- 91
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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