Galaxy Morphology Network: A Convolutional Neural Network Used to Study Morphology and Quenching in $sim 100,000$ SDSS and $sim 20,000$ CANDELS Galaxies. (arXiv:2006.14639v1 [astro-ph.GA])
<a href="http://arxiv.org/find/astro-ph/1/au:+Ghosh_A/0/1/0/all/0/1">Aritra Ghosh</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Urry_C/0/1/0/all/0/1">C. Megan Urry</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Wang_Z/0/1/0/all/0/1">Zhengdong Wang</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Schawinski_K/0/1/0/all/0/1">Kevin Schawinski</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Turp_D/0/1/0/all/0/1">Dennis Turp</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Powell_M/0/1/0/all/0/1">Meredith C. Powell</a>
We examine morphology-separated color-mass diagrams to study the quenching of
star formation in $sim 100,000$ ($zsim0$) Sloan Digital Sky Survey (SDSS) and
$sim 20,000$ ($zsim1$) Cosmic Assembly Near-Infrared Deep Extragalactic
Legacy Survey (CANDELS) galaxies. To classify galaxies morphologically, we
developed Galaxy Morphology Network (GaMorNet), a convolutional neural network
that classifies galaxies according to their bulge-to-total light ratio.
GaMorNet does not need a large training set of real data and can be applied to
data sets with a range of signal-to-noise ratios and spatial resolutions.
GaMorNet’s source code as well as the trained models are made public as part of
this work ( this http URL ). We first trained
GaMorNet on simulations of galaxies with a bulge and a disk component and then
transfer learned using $sim25%$ of each data set to achieve misclassification
rates of $lesssim5%$. The misclassified sample of galaxies is dominated by
small galaxies with low signal-to-noise ratios. Using the GaMorNet
classifications, we find that bulge- and disk-dominated galaxies have distinct
color-mass diagrams, in agreement with previous studies. For both SDSS and
CANDELS galaxies, disk-dominated galaxies peak in the blue cloud, across a
broad range of masses, consistent with the slow exhaustion of star-forming gas
with no rapid quenching. A small population of red disks is found at high mass
($sim14%$ of disks at $zsim0$ and $2%$ of disks at $z sim 1$). In
contrast, bulge-dominated galaxies are mostly red, with much smaller numbers
down toward the blue cloud, suggesting rapid quenching and fast evolution
across the green valley. This inferred difference in quenching mechanism is in
agreement with previous studies that used other morphology classification
techniques on much smaller samples at $zsim0$ and $zsim1$.
We examine morphology-separated color-mass diagrams to study the quenching of
star formation in $sim 100,000$ ($zsim0$) Sloan Digital Sky Survey (SDSS) and
$sim 20,000$ ($zsim1$) Cosmic Assembly Near-Infrared Deep Extragalactic
Legacy Survey (CANDELS) galaxies. To classify galaxies morphologically, we
developed Galaxy Morphology Network (GaMorNet), a convolutional neural network
that classifies galaxies according to their bulge-to-total light ratio.
GaMorNet does not need a large training set of real data and can be applied to
data sets with a range of signal-to-noise ratios and spatial resolutions.
GaMorNet’s source code as well as the trained models are made public as part of
this work ( this http URL ). We first trained
GaMorNet on simulations of galaxies with a bulge and a disk component and then
transfer learned using $sim25%$ of each data set to achieve misclassification
rates of $lesssim5%$. The misclassified sample of galaxies is dominated by
small galaxies with low signal-to-noise ratios. Using the GaMorNet
classifications, we find that bulge- and disk-dominated galaxies have distinct
color-mass diagrams, in agreement with previous studies. For both SDSS and
CANDELS galaxies, disk-dominated galaxies peak in the blue cloud, across a
broad range of masses, consistent with the slow exhaustion of star-forming gas
with no rapid quenching. A small population of red disks is found at high mass
($sim14%$ of disks at $zsim0$ and $2%$ of disks at $z sim 1$). In
contrast, bulge-dominated galaxies are mostly red, with much smaller numbers
down toward the blue cloud, suggesting rapid quenching and fast evolution
across the green valley. This inferred difference in quenching mechanism is in
agreement with previous studies that used other morphology classification
techniques on much smaller samples at $zsim0$ and $zsim1$.
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