Galaxy Morphology Network: A Convolutional Neural Network Used to Study Morphology and Quenching in ∼100,000 SDSS and ∼20,000 CANDELS Galaxies

We examine morphology-separated color-mass diagrams to study the quenching of star formation in ∼100,000 (z ∼ 0) Sloan Digital Sky Survey (SDSS) and ∼20,000 (z ∼ 1) 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. We first trained GAMORNET on simulations of galaxies with a bulge and a disk component and then transfer learned using ∼25% of each data set to achieve misclassification rates of ≲5%. 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 (∼14% of disks at z ∼ 0 and 2% of disks at z ∼ 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 z ∼ 0 and z ∼ 1.