%0 Generic %1 chartab2022machine %A Chartab, Nima %A Mobasher, Bahram %A Cooray, Asantha %A Hemmati, Shoubaneh %A Sattari, Zahra %A Ferguson, Henry C. %A Sanders, David B. %A Weaver, John R. %A Stern, Daniel %A McCracken, Henry J. %A Masters, Daniel C. %A Toft, Sune %A Capak, Peter L. %A Davidzon, Iary %A Dickinson, Mark %A Rhodes, Jason %A Moneti, Andrea %A Ilbert, Olivier %A Zalesky, Lukas %A McPartland, Conor %A Szapudi, Istvan %A Koekemoer, Anton M. %A Teplitz, Harry I. %A Giavalisco, Mauro %D 2022 %K library %T A Machine Learning Approach to Predict Missing Flux Densities in Multi-band Galaxy Surveys %U http://arxiv.org/abs/2208.14781 %X 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 a desired accuracy. As a proof of concept, using the recently updated COSMOS catalog (COSMOS2020), we identify the most relevant wavebands 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 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 inter-correlation between the bands, we train a model to predict UVISTA observations in near-IR from H20-like observations. We find that magnitudes in $YJH$ bands can be simulated/predicted with an accuracy of $1\sigma$ 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.

@misc{chartab2022machine, 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 a desired accuracy. As a proof of concept, using the recently updated COSMOS catalog (COSMOS2020), we identify the most relevant wavebands 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 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 inter-correlation between the bands, we train a model to predict UVISTA observations in near-IR from H20-like observations. We find that magnitudes in $YJH$ bands can be simulated/predicted with an accuracy of $1\sigma$ mag scatter $\lesssim 0.2$ 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.}, added-at = {2022-09-01T08:17:37.000+0200}, author = {Chartab, Nima and Mobasher, Bahram and Cooray, Asantha and Hemmati, Shoubaneh and Sattari, Zahra and Ferguson, Henry C. and Sanders, David B. and Weaver, John R. and Stern, Daniel and McCracken, Henry J. and Masters, Daniel C. and Toft, Sune and Capak, Peter L. and Davidzon, Iary and Dickinson, Mark and Rhodes, Jason and Moneti, Andrea and Ilbert, Olivier and Zalesky, Lukas and McPartland, Conor and Szapudi, Istvan and Koekemoer, Anton M. and Teplitz, Harry I. and Giavalisco, Mauro}, biburl = {https://www.bibsonomy.org/bibtex/2bb114a9ce1221c40ba92e8079a9146e0/gpkulkarni}, description = {A Machine Learning Approach to Predict Missing Flux Densities in Multi-band Galaxy Surveys}, interhash = {eab634692fcef790e54bf0ce12a9bd7a}, intrahash = {bb114a9ce1221c40ba92e8079a9146e0}, keywords = {library}, note = {cite arxiv:2208.14781Comment: 15 pages, 14 figures, accepted for publication in ApJ}, timestamp = {2022-09-01T08:17:37.000+0200}, title = {A Machine Learning Approach to Predict Missing Flux Densities in Multi-band Galaxy Surveys}, url = {http://arxiv.org/abs/2208.14781}, year = 2022 }