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A Machine-learning Approach to Predict Missing Flux Densities in Multiband Galaxy Surveys

Nima ChartabDepartment of Physics and Astronomy, University of California, Irvine, CA 92697, USAB. MobasherDepartment of Physics and Astronomy, University of California, Riverside, 900 University Avenue, Riverside, CA 92521, USAAsantha CoorayDepartment of Physics and Astronomy, University of California, Irvine, CA 92697, USAShoubaneh HemmatiInfrared Processing and Analysis Center, California Institute of Technology, Pasadena, CA 91125, USAZahra SattariDepartment of Physics and Astronomy, University of California, Riverside, 900 University Avenue, Riverside, CA 92521, USAHenry C. FergusonSpace Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USAD. B. SandersInstitute for Astronomy (IfA), University of Hawaii, 2680 Woodlawn Drive, Honolulu, HI 96822, USAJ. R. WeaverCosmic Dawn Center (DAWN), DenmarkDaniel SternJet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, USAH. J. McCrackenInstitut d’Astrophysique de Paris, UMR 7095, CNRS, and Sorbonne Université, 98 bis boulevard Arago, F-75014 Paris, FranceDaniel MastersInfrared Processing and Analysis Center, California Institute of Technology, Pasadena, CA 91125, USASune ToftCosmic Dawn Center (DAWN), DenmarkP. CapakInfrared Processing and Analysis Center, California Institute of Technology, Pasadena, CA 91125, USAI. DavidzonCosmic Dawn Center (DAWN), DenmarkMark DickinsonNational Optical Astronomy Observatories, 950 N. Cherry Avenue, Tucson, AZ 85719, USAJason RhodesJet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, USAA. MonetiInstitut d’Astrophysique de Paris, UMR 7095, CNRS, and Sorbonne Université, 98 bis boulevard Arago, F-75014 Paris, FranceO. IlbertAix Marseille Univ, CNRS, LAM, Laboratoire d’Astrophysique de Marseille, Marseille, FranceL. ZaleskyInstitute for Astronomy (IfA), University of Hawaii, 2680 Woodlawn Drive, Honolulu, HI 96822, USAConor McPartlandInstitute for Astronomy (IfA), University of Hawaii, 2680 Woodlawn Drive, Honolulu, HI 96822, USAI. SzapudiInstitute for Astronomy (IfA), University of Hawaii, 2680 Woodlawn Drive, Honolulu, HI 96822, USAA. M. KoekemoerSpace Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USAHarry I. TeplitzInfrared Processing and Analysis Center, California Institute of Technology, Pasadena, CA 91125, USAMauro GiavaliscoDepartment of Astronomy, University of Massachusetts, 710 N. Pleasant Street, Amherst, MA 01003, USA

2023en

ABI

Annotatsiya

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 of i < 25 AB mag galaxies. We find that with the available i -band fluxes, r , u , IRAC/ ch 2, and z bands provide most of the information regarding the redshift with importance decreasing from r band to z band. We also find that for the same sample, IRAC/ ch 2, Y , r , and u bands 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 the YJH bands can be simulated/predicted with an accuracy of 1 σ mag 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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DOI: 10.3847/1538-4357/acacf5

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