论文标题
用于从SDSS的星系形态分类的机器学习技术。 ii。基于图像的星系的形态目录为0.02 <z <0.1
Machine learning technique for morphological classification of galaxies from SDSS. II. The image-based morphological catalogs of galaxies at 0.02<z<0.1
论文作者
论文摘要
我们使用卷积神经网络模型应用了基于图像的方法，以$ -24^{m} <m_ {r} <-19.4^{M} $从SDSS DR9中使用$ -24^{M} <m_ {r} <-19.4^{M} $。我们将其分为两个子样本：SDSS DR9 Galaxy数据集和Galaxy Zoo 2（GZ2）数据集，分别将其视为推理和培训数据集。结果，我们在0.02 <z <0.1处创建了315782星系的形态目录，其中首先定义了216148个星系（推理数据集）的形态五个类别和34个详细特征（条，环，螺旋臂数，合并等），由基于图像的CNN CNN分类器定义。在其余的星系中，最初的形态分类被重新分配，如GZ2项目中。 我们的方法表明，对于五类形态预测，形态学分类的有希望的表现，除了雪茄形（75％）和完全圆形（83％）星系，其准确性的93％。主要结果在19468年的目录中呈现，完全圆形，27321圆形圆形，3235个雪茄形，4099 Edge-On，18615螺旋和72738所研究的SDSS样品的一般低红色速度星系。至于星系的分类，通过其详细的结构形态特征，我们的CNN模型的准确性在92-99％的范围内取决于特征，许多星系在推理数据集中具有给定特征的星系以及星系图像质量。我们证明，具有对抗性验证和对抗图像数据增强CNN模型的含义可改善使用$ M_ {R} $ <17.7的较小和淡淡的SDSS星系的分类。
We applied the image-based approach with a convolutional neural network model to the sample of low-redshifts galaxies with $-24^{m}<M_{r}<-19.4^{m}$ from the SDSS DR9. We divided it into two subsamples, SDSS DR9 galaxy dataset and Galaxy Zoo 2 (GZ2) dataset, considering them as the inference and training datasets, respectively. As a result, we created the morphological catalog of 315782 galaxies at 0.02<z<0.1, where morphological five classes and 34 detailed features (bar, rings, number of spiral arms, mergers, etc.) were first defined for 216148 galaxies (inference dataset) by the image-based CNN classifier. For the rest of galaxies the initial morphological classification was re-assigned as in the GZ2 project. Our method shows the promising performance of morphological classification attaining more 93 % of accuracy for five classes morphology prediction except the cigar-shaped (75 %) and completely rounded (83 %) galaxies. Main results are presented in the catalog of 19468 completely rounded, 27321 rounded in-between, 3235 cigar-shaped, 4099 edge-on, 18615 spiral, and 72738 general low-redshift galaxies of the studied SDSS sample. As for the classification of galaxies by their detailed structural morphological features, our CNN model gives the accuracy in the range of 92-99 % depending on features, a number of galaxies with the given feature in the inference dataset, and the galaxy image quality. We demonstrate that implication of the CNN model with adversarial validation and adversarial image data augmentation improves classification of smaller and fainter SDSS galaxies with $m_{r}$ <17.7.
