We examine morphology-separated color-mass diagrams to study the quenching of star formation in $\sim 100,000$ ($z\sim0$) Sloan Digital Sky Survey (SDSS) and $\sim 20,000$ ($z\sim1$) 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 $z\sim0$ 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 $z\sim0$ and $z\sim1$.

中文翻译：

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Galaxy Morphology Network：一种卷积神经网络，用于研究 ~100,000 SDSS 和 ~20,000 CANDELS 星系的形态学和淬灭

我们研究了形态分离的颜色质量图，以研究 $\sim 100,000$ ($z\sim0$) 斯隆数字巡天 (SDSS) 和 $\sim 20,000$ ($z\sim1$) Cosmic 中恒星形成的猝灭组装近红外深河外遗产调查 (CANDELS) 星系。为了在形态上对星系进行分类，我们开发了 Galaxy Morphology Network (GaMorNet)，这是一种卷积神经网络，可根据星系的凸起与总光之比对星系进行分类。GaMorNet 不需要大量的真实数据训练集，可以应用于具有一系列信噪比和空间分辨率的数据集。GaMorNet 的源代码以及经过训练的模型作为这项工作的一部分公开（此 http URL）。我们首先在具有凸起和磁盘组件的星系模拟上训练 GaMorNet，然后使用每个数据集的 $\sim25\%$ 进行迁移学习，以实现 $\lesssim5\%$ 的错误分类率。错误分类的星系样本主要是信噪比低的小星系。使用 GaMorNet 分类，我们发现膨胀和圆盘主导的星系具有不同的色质量图，与之前的研究一致。对于 SDSS 和 CANDELS 星系，盘状星系在蓝云中达到峰值，质量范围很广，这与恒星形成气体缓慢耗尽而没有快速淬灭一致。发现了少量的红色圆盘（$\sim14\%$ 位于 $z\sim0$ 的圆盘和 $2\%$ 的圆盘位于 $z\sim 1$）。相比之下，以膨胀为主的星系大多是红色的，向蓝云下降的数字要小得多，这表明整个绿色山谷的快速淬灭和快速演化。这种淬灭机制的推断差异与之前的研究一致，这些研究在 $z\sim0$ 和 $z\sim1$ 上对更小的样本使用其他形态分类技术。