Morphology-assisted galaxy mass-to-light predictions using deep learning. (arXiv:1903.05091v1 [astro-ph.GA])
<a href="http://arxiv.org/find/astro-ph/1/au:+Dobbels_W/0/1/0/all/0/1">Wouter Dobbels</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Krier_S/0/1/0/all/0/1">Serge Krier</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Pirson_S/0/1/0/all/0/1">Stephan Pirson</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Viaene_S/0/1/0/all/0/1">Sébastien Viaene</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Geyter_G/0/1/0/all/0/1">Gert De Geyter</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Salim_S/0/1/0/all/0/1">Samir Salim</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Baes_M/0/1/0/all/0/1">Maarten Baes</a>
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. 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. 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. 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. 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.
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. 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. 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. 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. 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.
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