Volume 624, April 2019
|Number of page(s)||17|
|Published online||18 April 2019|
Morphology-assisted galaxy mass-to-light predictions using deep learning
Sterrenkundig Observatorium, Universiteit Gent, Krijgslaan 281 S9, 9000 Gent, Belgium
2 Rue de l’Institut 32c, 1330 Rixensart, Belgium
3 Ingenico ePayments, Boulevard de la Woluwe 102, 1200 Woluwe-Saint-Lambert, Belgium
4 Centre for Astrophysics Research, University of Hertfordshire, College Lane, Hatfield AL10 9AB, UK
5 Department of Astronomy, Indiana University, Bloomington, IN 47404, USA
Accepted: 7 March 2019
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.
Key words: galaxies: fundamental parameters / galaxies: stellar content
© ESO 2019
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