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

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

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...

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Bibliographic Details
Main Authors: 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
Format: Article
Language:English
Published: IOP Publishing 2023-01-01
Series:The Astrophysical Journal
Subjects:
Astronomy data analysis
Astronomy data visualization
Galaxy evolution
Online Access:https://doi.org/10.3847/1538-4357/acacf5

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