A Machine-learning Approach to Predict Missing Flux Densities in Multiband Galaxy Surveys
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A Machine-learning Approach to Predict Missing Flux Densities in Multiband Galaxy Surveys. / 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, Istvan; Koekemoer, Anton M.; Teplitz, Harry I.; Giavalisco, Mauro.
I: Astrophysical Journal, Bind 942, Nr. 2, 91, 17.01.2023.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - A Machine-learning Approach to Predict Missing Flux Densities in Multiband Galaxy Surveys
AU - Chartab, Nima
AU - Mobasher, Bahram
AU - Cooray, Asantha R.
AU - Hemmati, Shoubaneh
AU - Sattari, Zahra
AU - Ferguson, Henry C.
AU - Sanders, David B.
AU - Weaver, John R.
AU - Stern, Daniel K.
AU - McCracken, Henry J.
AU - Masters, Daniel C.
AU - Toft, Sune
AU - Capak, Peter L.
AU - Davidzon, Iary
AU - Dickinson, Mark E.
AU - Rhodes, Jason
AU - Moneti, Andrea
AU - Ilbert, Olivier
AU - Zalesky, Lukas
AU - McPartland, Conor J. R.
AU - Szapudi, Istvan
AU - Koekemoer, Anton M.
AU - Teplitz, Harry I.
AU - Giavalisco, Mauro
PY - 2023/1/17
Y1 - 2023/1/17
N2 - 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 1 sigma mag scatter less than or similar to 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.
AB - 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 1 sigma mag scatter less than or similar to 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.
KW - PHOTOMETRIC REDSHIFTS
KW - STELLAR
KW - EVOLUTION
U2 - 10.3847/1538-4357/acacf5
DO - 10.3847/1538-4357/acacf5
M3 - Journal article
VL - 942
JO - Astrophysical Journal
JF - Astrophysical Journal
SN - 0004-637X
IS - 2
M1 - 91
ER -
ID: 337693608