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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 (+12 others)
2023 Astrophysical Journal  

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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
more » ... 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.
doi:10.3847/1538-4357/acacf5 fatcat:3ejogdzun5a7vnti4ittypemau
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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, Peter L. Capak, Iary Davidzon, Mark E. Dickinson, Jason Rhodes, Andrea Moneti, Olivier Ilbert, Lukas Zalesky, Conor J. R. McPartland, István Szapudi, Anton M. Koekemoer, Harry I. Teplitz, Mauro Giavalisco. "A Machine-learning Approach to Predict Missing Flux Densities in Multiband Galaxy Surveys." Astrophysical Journal 942.2 (2023) 91