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

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

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.

In: Astrophysical Journal, Vol. 942, No. 2, 91, 17.01.2023.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Chartab, N, Mobasher, B, Cooray, AR, Hemmati, S, Sattari, Z, Ferguson, HC, Sanders, DB, Weaver, JR, Stern, DK, McCracken, HJ, Masters, DC, Toft, S, Capak, PL, Davidzon, I, Dickinson, ME, Rhodes, J, Moneti, A, Ilbert, O, Zalesky, L, McPartland, CJR, Szapudi, I, Koekemoer, AM, Teplitz, HI & Giavalisco, M 2023, 'A Machine-learning Approach to Predict Missing Flux Densities in Multiband Galaxy Surveys', Astrophysical Journal, vol. 942, no. 2, 91. https://doi.org/10.3847/1538-4357/acacf5

APA

Chartab, N., Mobasher, B., Cooray, A. R., Hemmati, S., Sattari, Z., Ferguson, H. C., Sanders, D. B., Weaver, J. R., Stern, D. K., McCracken, H. J., Masters, D. C., Toft, S., Capak, P. L., Davidzon, I., Dickinson, M. E., Rhodes, J., Moneti, A., Ilbert, O., Zalesky, L., ... Giavalisco, M. (2023). A Machine-learning Approach to Predict Missing Flux Densities in Multiband Galaxy Surveys. Astrophysical Journal, 942(2), [91]. https://doi.org/10.3847/1538-4357/acacf5

Vancouver

Chartab N, Mobasher B, Cooray AR, Hemmati S, Sattari Z, Ferguson HC et al. A Machine-learning Approach to Predict Missing Flux Densities in Multiband Galaxy Surveys. Astrophysical Journal. 2023 Jan 17;942(2). 91. https://doi.org/10.3847/1538-4357/acacf5

Author

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. / A Machine-learning Approach to Predict Missing Flux Densities in Multiband Galaxy Surveys. In: Astrophysical Journal. 2023 ; Vol. 942, No. 2.

Bibtex

@article{2319e57f4e9d40f79e6a9264f54e8925,
title = "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/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.",
keywords = "PHOTOMETRIC REDSHIFTS, STELLAR, EVOLUTION",
author = "Nima Chartab and Bahram Mobasher and Cooray, {Asantha R.} and Shoubaneh Hemmati and Zahra Sattari 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 Sune Toft and Capak, {Peter L.} and Iary Davidzon and Dickinson, {Mark E.} and Jason Rhodes and Andrea Moneti and Olivier Ilbert and Lukas Zalesky and McPartland, {Conor J. R.} and Istvan Szapudi and Koekemoer, {Anton M.} and Teplitz, {Harry I.} and Mauro Giavalisco",
year = "2023",
month = jan,
day = "17",
doi = "10.3847/1538-4357/acacf5",
language = "English",
volume = "942",
journal = "Astrophysical Journal",
issn = "0004-637X",
publisher = "Institute of Physics Publishing, Inc",
number = "2",

}

RIS

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