Planck-dust-allsky

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

January 2023 • 2023ApJ...942...91C

Authors • Chartab, Nima • Mobasher, Bahram • Cooray, Asantha R. • Hemmati, Shoubaneh • Sattari, Zahra • Ferguson, Henry C. • Sanders, David B. • Weaver, John R. • Stern, Daniel K. • McCracken, Henry J. • Masters, Daniel C. • Toft, Sune • Capak, Peter L. • Davidzon, Iary • Dickinson, Mark E. • Rhodes, Jason • Moneti, Andrea • Ilbert, Olivier • Zalesky, Lukas • McPartland, Conor J. R. • Szapudi, István • Koekemoer, Anton M. • Teplitz, Harry I. • Giavalisco, Mauro

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/ch2, 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/ch2, 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 1σ 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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