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

Chartab, Nima and Mobasher, Bahram and Cooray, Asantha R. and Hemmati, Shoubaneh and Sattari, Zahra and Ferguson, Henry C. and Sanders, David B. and Weaver, John R. and Stern, Daniel K. and McCracken, Henry J. and Masters, Daniel C. and Toft, Sune and Capak, Peter L. and Davidzon, Iary and Dickinson, Mark E. and Rhodes, Jason and Moneti, Andrea and Ilbert, Olivier and Zalesky, Lukas and McPartland, Conor J. R. and Szapudi, István and Koekemoer, Anton M. and Teplitz, Harry I. and Giavalisco, Mauro (2023) A Machine-learning Approach to Predict Missing Flux Densities in Multiband Galaxy Surveys. Astrophysical Journal, 942 (2). Art. No. 91. ISSN 0004-637X. doi:10.3847/1538-4357/acacf5.

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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 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.

Item Type:Article
Related URLs:
URLURL TypeDescription
Chartab, Nima0000-0003-3691-937X
Mobasher, Bahram0000-0001-5846-4404
Cooray, Asantha R.0000-0002-3892-0190
Hemmati, Shoubaneh0000-0003-2226-5395
Sattari, Zahra0000-0002-0364-1159
Ferguson, Henry C.0000-0001-7113-2738
Sanders, David B.0000-0002-1233-9998
Weaver, John R.0000-0003-1614-196X
Stern, Daniel K.0000-0003-2686-9241
McCracken, Henry J.0000-0002-9489-7765
Masters, Daniel C.0000-0001-5382-6138
Toft, Sune0000-0003-3631-7176
Capak, Peter L.0000-0003-3578-6843
Davidzon, Iary0000-0002-2951-7519
Dickinson, Mark E.0000-0001-5414-5131
Rhodes, Jason0000-0002-4485-8549
Ilbert, Olivier0000-0002-7303-4397
Zalesky, Lukas0000-0001-5680-2326
McPartland, Conor J. R.0000-0003-0639-025X
Szapudi, István0000-0003-2274-0301
Koekemoer, Anton M.0000-0002-6610-2048
Teplitz, Harry I.0000-0002-7064-5424
Giavalisco, Mauro0000-0002-7831-8751
Additional Information:Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. We thank the anonymous referee for providing insightful comments and suggestions that improved the quality of this work. N.C. and A.C. acknowledge support from NASA ADAP 80NSSC20K0437. I.D. has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 896225.
Group:Infrared Processing and Analysis Center (IPAC)
Funding AgencyGrant Number
Marie Curie Fellowship896225
Issue or Number:2
Record Number:CaltechAUTHORS:20230207-728273600.8
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:119086
Deposited By: Research Services Depository
Deposited On:14 Mar 2023 23:29
Last Modified:14 Mar 2023 23:29

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