A Machine-learning Approach to Predict Missing Flux Densities in Multiband Galaxy Surveys - Nima Chartab; Bahram Mobasher; Asantha R. Cooray; Shoubaneh Hemmati; Zahra Sattari; Henry C. Ferguson; David B. Sanders; John R. Weaver; Daniel K. Stern; Henry J. McCracken; Daniel C. Masters; Sune Toft; et al. (The Astrophysical Journal, 2023)
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.