Galaxy Morphology Network: A Convolutional Neural Network Used to Study Morphology and Quenching in $\sim 100,000$ SDSS and $\sim 20,000$ CANDELS Galaxies
Aritra Ghosh, C. Megan Urry, Zhengdong Wang, Kevin Schawinski, Dennis, Turp, Meredith C. Powell

TL;DR
This paper introduces GaMorNet, a convolutional neural network for galaxy morphology classification, enabling large-scale analysis of galaxy quenching mechanisms across extensive SDSS and CANDELS datasets.
Contribution
GaMorNet is a novel CNN that classifies galaxy morphology without extensive real-data training, applicable across various data qualities, and is publicly available.
Findings
Bulge- and disk-dominated galaxies show distinct color-mass diagrams.
Disk-dominated galaxies mainly reside in the blue cloud, indicating slow star formation exhaustion.
Bulge-dominated galaxies are mostly red, suggesting rapid quenching.
Abstract
We examine morphology-separated color-mass diagrams to study the quenching of star formation in () Sloan Digital Sky Survey (SDSS) and () 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 ( http://www.astro.yale.edu/aghosh/gamornet.html ). We first trained GaMorNet on simulations of galaxies with a bulge and a disk component and then transfer learned using of each data set…
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