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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Autor*in:	Nima Chartab [verfasserIn] Bahram Mobasher [verfasserIn] Asantha R. Cooray [verfasserIn] Shoubaneh Hemmati [verfasserIn] Zahra Sattari [verfasserIn] Henry C. Ferguson [verfasserIn] David B. Sanders [verfasserIn] John R. Weaver [verfasserIn] Daniel K. Stern [verfasserIn] Henry J. McCracken [verfasserIn] Daniel C. Masters [verfasserIn] Sune Toft [verfasserIn] Peter L. Capak [verfasserIn] Iary Davidzon [verfasserIn] Mark E. Dickinson [verfasserIn] Jason Rhodes [verfasserIn] Andrea Moneti [verfasserIn] Olivier Ilbert [verfasserIn] Lukas Zalesky [verfasserIn] Conor J. R. McPartland [verfasserIn] István Szapudi [verfasserIn] Anton M. Koekemoer [verfasserIn] Harry I. Teplitz [verfasserIn] Mauro Giavalisco [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Astronomy data analysis Astronomy data visualization Galaxy evolution

Übergeordnetes Werk:	In: The Astrophysical Journal - IOP Publishing, 2022, 942(2023), 2, p 91
Übergeordnetes Werk:	volume:942 ; year:2023 ; number:2, p 91

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3847/1538-4357/acacf5

Katalog-ID:	DOAJ089163303

Internformat


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allfields	10.3847/1538-4357/acacf5 doi (DE-627)DOAJ089163303 (DE-599)DOAJbe2cf23df81a47d7b52f3a3dc9c08a0d DE-627 ger DE-627 rakwb eng QB460-466 Nima Chartab verfasserin aut A Machine-learning Approach to Predict Missing Flux Densities in Multiband Galaxy Surveys 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Astronomy data analysis Astronomy data visualization Galaxy evolution Astrophysics Bahram Mobasher verfasserin aut Asantha R. Cooray verfasserin aut Shoubaneh Hemmati verfasserin aut Zahra Sattari verfasserin aut Henry C. Ferguson verfasserin aut David B. Sanders verfasserin aut John R. Weaver verfasserin aut Daniel K. Stern verfasserin aut Henry J. McCracken verfasserin aut Daniel C. Masters verfasserin aut Sune Toft verfasserin aut Peter L. Capak verfasserin aut Iary Davidzon verfasserin aut Mark E. Dickinson verfasserin aut Jason Rhodes verfasserin aut Andrea Moneti verfasserin aut Olivier Ilbert verfasserin aut Lukas Zalesky verfasserin aut Conor J. R. McPartland verfasserin aut István Szapudi verfasserin aut Anton M. Koekemoer verfasserin aut Harry I. Teplitz verfasserin aut Mauro Giavalisco verfasserin aut In The Astrophysical Journal IOP Publishing, 2022 942(2023), 2, p 91 (DE-627)269019219 (DE-600)1473835-1 15384357 nnns volume:942 year:2023 number:2, p 91 https://doi.org/10.3847/1538-4357/acacf5 kostenfrei https://doaj.org/article/be2cf23df81a47d7b52f3a3dc9c08a0d kostenfrei https://doi.org/10.3847/1538-4357/acacf5 kostenfrei https://doaj.org/toc/1538-4357 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_2088 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 942 2023 2, p 91
spelling	10.3847/1538-4357/acacf5 doi (DE-627)DOAJ089163303 (DE-599)DOAJbe2cf23df81a47d7b52f3a3dc9c08a0d DE-627 ger DE-627 rakwb eng QB460-466 Nima Chartab verfasserin aut A Machine-learning Approach to Predict Missing Flux Densities in Multiband Galaxy Surveys 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Astronomy data analysis Astronomy data visualization Galaxy evolution Astrophysics Bahram Mobasher verfasserin aut Asantha R. Cooray verfasserin aut Shoubaneh Hemmati verfasserin aut Zahra Sattari verfasserin aut Henry C. Ferguson verfasserin aut David B. Sanders verfasserin aut John R. Weaver verfasserin aut Daniel K. Stern verfasserin aut Henry J. McCracken verfasserin aut Daniel C. Masters verfasserin aut Sune Toft verfasserin aut Peter L. Capak verfasserin aut Iary Davidzon verfasserin aut Mark E. Dickinson verfasserin aut Jason Rhodes verfasserin aut Andrea Moneti verfasserin aut Olivier Ilbert verfasserin aut Lukas Zalesky verfasserin aut Conor J. R. McPartland verfasserin aut István Szapudi verfasserin aut Anton M. Koekemoer verfasserin aut Harry I. Teplitz verfasserin aut Mauro Giavalisco verfasserin aut In The Astrophysical Journal IOP Publishing, 2022 942(2023), 2, p 91 (DE-627)269019219 (DE-600)1473835-1 15384357 nnns volume:942 year:2023 number:2, p 91 https://doi.org/10.3847/1538-4357/acacf5 kostenfrei https://doaj.org/article/be2cf23df81a47d7b52f3a3dc9c08a0d kostenfrei https://doi.org/10.3847/1538-4357/acacf5 kostenfrei https://doaj.org/toc/1538-4357 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_2088 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 942 2023 2, p 91
allfields_unstemmed	10.3847/1538-4357/acacf5 doi (DE-627)DOAJ089163303 (DE-599)DOAJbe2cf23df81a47d7b52f3a3dc9c08a0d DE-627 ger DE-627 rakwb eng QB460-466 Nima Chartab verfasserin aut A Machine-learning Approach to Predict Missing Flux Densities in Multiband Galaxy Surveys 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Astronomy data analysis Astronomy data visualization Galaxy evolution Astrophysics Bahram Mobasher verfasserin aut Asantha R. Cooray verfasserin aut Shoubaneh Hemmati verfasserin aut Zahra Sattari verfasserin aut Henry C. Ferguson verfasserin aut David B. Sanders verfasserin aut John R. Weaver verfasserin aut Daniel K. Stern verfasserin aut Henry J. McCracken verfasserin aut Daniel C. Masters verfasserin aut Sune Toft verfasserin aut Peter L. Capak verfasserin aut Iary Davidzon verfasserin aut Mark E. Dickinson verfasserin aut Jason Rhodes verfasserin aut Andrea Moneti verfasserin aut Olivier Ilbert verfasserin aut Lukas Zalesky verfasserin aut Conor J. R. McPartland verfasserin aut István Szapudi verfasserin aut Anton M. Koekemoer verfasserin aut Harry I. Teplitz verfasserin aut Mauro Giavalisco verfasserin aut In The Astrophysical Journal IOP Publishing, 2022 942(2023), 2, p 91 (DE-627)269019219 (DE-600)1473835-1 15384357 nnns volume:942 year:2023 number:2, p 91 https://doi.org/10.3847/1538-4357/acacf5 kostenfrei https://doaj.org/article/be2cf23df81a47d7b52f3a3dc9c08a0d kostenfrei https://doi.org/10.3847/1538-4357/acacf5 kostenfrei https://doaj.org/toc/1538-4357 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_2088 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 942 2023 2, p 91
allfieldsGer	10.3847/1538-4357/acacf5 doi (DE-627)DOAJ089163303 (DE-599)DOAJbe2cf23df81a47d7b52f3a3dc9c08a0d DE-627 ger DE-627 rakwb eng QB460-466 Nima Chartab verfasserin aut A Machine-learning Approach to Predict Missing Flux Densities in Multiband Galaxy Surveys 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Astronomy data analysis Astronomy data visualization Galaxy evolution Astrophysics Bahram Mobasher verfasserin aut Asantha R. Cooray verfasserin aut Shoubaneh Hemmati verfasserin aut Zahra Sattari verfasserin aut Henry C. Ferguson verfasserin aut David B. Sanders verfasserin aut John R. Weaver verfasserin aut Daniel K. Stern verfasserin aut Henry J. McCracken verfasserin aut Daniel C. Masters verfasserin aut Sune Toft verfasserin aut Peter L. Capak verfasserin aut Iary Davidzon verfasserin aut Mark E. Dickinson verfasserin aut Jason Rhodes verfasserin aut Andrea Moneti verfasserin aut Olivier Ilbert verfasserin aut Lukas Zalesky verfasserin aut Conor J. R. McPartland verfasserin aut István Szapudi verfasserin aut Anton M. Koekemoer verfasserin aut Harry I. Teplitz verfasserin aut Mauro Giavalisco verfasserin aut In The Astrophysical Journal IOP Publishing, 2022 942(2023), 2, p 91 (DE-627)269019219 (DE-600)1473835-1 15384357 nnns volume:942 year:2023 number:2, p 91 https://doi.org/10.3847/1538-4357/acacf5 kostenfrei https://doaj.org/article/be2cf23df81a47d7b52f3a3dc9c08a0d kostenfrei https://doi.org/10.3847/1538-4357/acacf5 kostenfrei https://doaj.org/toc/1538-4357 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_2088 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 942 2023 2, p 91
allfieldsSound	10.3847/1538-4357/acacf5 doi (DE-627)DOAJ089163303 (DE-599)DOAJbe2cf23df81a47d7b52f3a3dc9c08a0d DE-627 ger DE-627 rakwb eng QB460-466 Nima Chartab verfasserin aut A Machine-learning Approach to Predict Missing Flux Densities in Multiband Galaxy Surveys 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Astronomy data analysis Astronomy data visualization Galaxy evolution Astrophysics Bahram Mobasher verfasserin aut Asantha R. Cooray verfasserin aut Shoubaneh Hemmati verfasserin aut Zahra Sattari verfasserin aut Henry C. Ferguson verfasserin aut David B. Sanders verfasserin aut John R. Weaver verfasserin aut Daniel K. Stern verfasserin aut Henry J. McCracken verfasserin aut Daniel C. Masters verfasserin aut Sune Toft verfasserin aut Peter L. Capak verfasserin aut Iary Davidzon verfasserin aut Mark E. Dickinson verfasserin aut Jason Rhodes verfasserin aut Andrea Moneti verfasserin aut Olivier Ilbert verfasserin aut Lukas Zalesky verfasserin aut Conor J. R. McPartland verfasserin aut István Szapudi verfasserin aut Anton M. Koekemoer verfasserin aut Harry I. Teplitz verfasserin aut Mauro Giavalisco verfasserin aut In The Astrophysical Journal IOP Publishing, 2022 942(2023), 2, p 91 (DE-627)269019219 (DE-600)1473835-1 15384357 nnns volume:942 year:2023 number:2, p 91 https://doi.org/10.3847/1538-4357/acacf5 kostenfrei https://doaj.org/article/be2cf23df81a47d7b52f3a3dc9c08a0d kostenfrei https://doi.org/10.3847/1538-4357/acacf5 kostenfrei https://doaj.org/toc/1538-4357 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_2088 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 942 2023 2, p 91
language	English
source	In The Astrophysical Journal 942(2023), 2, p 91 volume:942 year:2023 number:2, p 91
sourceStr	In The Astrophysical Journal 942(2023), 2, p 91 volume:942 year:2023 number:2, p 91
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Astronomy data analysis Astronomy data visualization Galaxy evolution Astrophysics
isfreeaccess_bool	true
container_title	The Astrophysical Journal
authorswithroles_txt_mv	Nima Chartab @@aut@@ Bahram Mobasher @@aut@@ Asantha R. Cooray @@aut@@ Shoubaneh Hemmati @@aut@@ Zahra Sattari @@aut@@ Henry C. Ferguson @@aut@@ David B. Sanders @@aut@@ John R. Weaver @@aut@@ Daniel K. Stern @@aut@@ Henry J. McCracken @@aut@@ Daniel C. Masters @@aut@@ Sune Toft @@aut@@ Peter L. Capak @@aut@@ Iary Davidzon @@aut@@ Mark E. Dickinson @@aut@@ Jason Rhodes @@aut@@ Andrea Moneti @@aut@@ Olivier Ilbert @@aut@@ Lukas Zalesky @@aut@@ Conor J. R. McPartland @@aut@@ István Szapudi @@aut@@ Anton M. Koekemoer @@aut@@ Harry I. Teplitz @@aut@@ Mauro Giavalisco @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	269019219
id	DOAJ089163303
language_de	englisch
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callnumber-first	Q - Science
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topic_title	QB460-466 A Machine-learning Approach to Predict Missing Flux Densities in Multiband Galaxy Surveys Astronomy data analysis Astronomy data visualization Galaxy evolution
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title	A Machine-learning Approach to Predict Missing Flux Densities in Multiband Galaxy Surveys
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author_browse	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
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title_sort	machine-learning approach to predict missing flux densities in multiband galaxy surveys
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title_auth	A Machine-learning Approach to Predict Missing Flux Densities in Multiband Galaxy Surveys
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	A Machine-learning Approach to Predict Missing Flux Densities in Multiband Galaxy Surveys
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