Advanced search
1 file | 39.09 MB

Morphology-assisted galaxy mass-to-light predictions using deep learning

Author
Organization
Abstract
Context. One of the most important properties of a galaxy is the total stellar mass, or equivalently the stellar mass-to-light ratio (M/L). It is not directly observable, but can be estimated from stellar population synthesis. Currently, a galaxy's M/L is typically estimated from global fluxes. For example, a single global g - i colour correlates well with the stellar M/L. Spectral energy distribution (SED) fitting can make use of all available fluxes and their errors to make a Bayesian estimate of the M/L. Aims: We want to investigate the possibility of using morphology information to assist predictions of M/L. Our first goal is to develop and train a method that only requires a g-band image and redshift as input. This will allows us to study the correlation between M/L and morphology. Next, we can also include the i-band flux, and determine if morphology provides additional constraints compared to a method that only uses g- and i-band fluxes. Methods: We used a machine learning pipeline that can be split in two steps. First, we detected morphology features with a convolutional neural network. These are then combined with redshift, pixel size and g-band luminosity features in a gradient boosting machine. Our training target was the M/L acquired from the GALEX-SDSS-WISE Legacy Catalog, which uses global SED fitting and contains galaxies with z ˜ 0.1. Results: Morphology is a useful attribute when no colour information is available, but can not outperform colour methods on its own. When we combine the morphology features with global g- and i-band luminosities, we find an improved estimate compared to a model which does not make use of morphology. Conclusions: While our method was trained to reproduce global SED fitted M/L, galaxy morphology gives us an important additional constraint when using one or two bands. Our framework can be extended to other problems to make use of morphological information.
Keywords
galaxies: fundamental parameters, galaxies: stellar content

Downloads

  • Dobbels et al. 2019.pdf
    • full text
    • |
    • open access
    • |
    • PDF
    • |
    • 39.09 MB

Citation

Please use this url to cite or link to this publication:

Chicago
Dobbels, Wouter, S. Krier, S. Pirson, Sébastien Viaene, Gert De Geyter, S. Salim, and Maarten Baes. 2019. “Morphology-assisted Galaxy Mass-to-light Predictions Using Deep Learning.” Astronomy & Astrophysics 624.
APA
Dobbels, W., Krier, S., Pirson, S., Viaene, S., De Geyter, G., Salim, S., & Baes, M. (2019). Morphology-assisted galaxy mass-to-light predictions using deep learning. Astronomy & Astrophysics, 624.
Vancouver
1.
Dobbels W, Krier S, Pirson S, Viaene S, De Geyter G, Salim S, et al. Morphology-assisted galaxy mass-to-light predictions using deep learning. Astronomy & Astrophysics. EDP Sciences; 2019;624.
MLA
Dobbels, Wouter et al. “Morphology-assisted Galaxy Mass-to-light Predictions Using Deep Learning.” Astronomy & Astrophysics 624 (2019): n. pag. Print.
@article{8614555,
  abstract     = {Context. One of the most important properties of a galaxy is the total stellar mass, or equivalently the stellar mass-to-light ratio (M/L). It is not directly observable, but can be estimated from stellar population synthesis. Currently, a galaxy's M/L is typically estimated from global fluxes. For example, a single global g - i colour correlates well with the stellar M/L. Spectral energy distribution (SED) fitting can make use of all available fluxes and their errors to make a Bayesian estimate of the M/L. 
Aims: We want to investigate the possibility of using morphology information to assist predictions of M/L. Our first goal is to develop and train a method that only requires a g-band image and redshift as input. This will allows us to study the correlation between M/L and morphology. Next, we can also include the i-band flux, and determine if morphology provides additional constraints compared to a method that only uses g- and i-band fluxes. 
Methods: We used a machine learning pipeline that can be split in two steps. First, we detected morphology features with a convolutional neural network. These are then combined with redshift, pixel size and g-band luminosity features in a gradient boosting machine. Our training target was the M/L acquired from the GALEX-SDSS-WISE Legacy Catalog, which uses global SED fitting and contains galaxies with z {\textasciitilde} 0.1. 
Results: Morphology is a useful attribute when no colour information is available, but can not outperform colour methods on its own. When we combine the morphology features with global g- and i-band luminosities, we find an improved estimate compared to a model which does not make use of morphology. 
Conclusions: While our method was trained to reproduce global SED fitted M/L, galaxy morphology gives us an important additional constraint when using one or two bands. Our framework can be extended to other problems to make use of morphological information.},
  articleno    = {A102},
  author       = {Dobbels, Wouter and Krier, S. and Pirson, S. and Viaene, S{\'e}bastien and De Geyter, Gert and Salim, S. and Baes, Maarten},
  journal      = {Astronomy \& Astrophysics},
  language     = {eng},
  publisher    = {EDP Sciences},
  title        = {Morphology-assisted galaxy mass-to-light predictions using deep learning},
  url          = {http://dx.doi.org/10.1051/0004-6361/201834575},
  volume       = {624},
  year         = {2019},
}

Altmetric
View in Altmetric