论文标题
基于深度学习的泛星系星系的广泛形态的目录
A catalog of broad morphology of Pan-STARRS galaxies based on deep learning
论文作者
论文摘要
自主数字天空调查（例如Pan-Starrs）具有图像大量银河系和半乳酸对象的成像，图像数据的较大且复杂的性质可增强自动化的使用。在这里，我们描述了用于自动广泛形态注释的数据分析过程的设计和实施，并将其应用于Pan-Starrs DR1的数据。该过程基于过滤器，然后是两步卷积神经网络（CNN）分类。通过使用增强和平衡的手动分类星系产生培训样品。通过与先前广泛的SDSS星系形态目录中包含的Pan-Stars的注释进行比较，评估了结果的准确性。我们的分析表明，与多个过滤器结合使用的CNN是注释星系并删除不洁图像的有效方法。该目录包含1,662,190个星系的形态标签，精度约为95％。通过选择高于某些置信阈值的标签可以进一步提高准确性。该目录可公开使用。
Autonomous digital sky surveys such as Pan-STARRS have the ability to image a very large number of galactic and extra-galactic objects, and the large and complex nature of the image data reinforces the use of automation. Here we describe the design and implementation of a data analysis process for automatic broad morphology annotation of galaxies, and applied it to the data of Pan-STARRS DR1. The process is based on filters followed by a two-step convolutional neural network (CNN) classification. Training samples are generated by using an augmented and balanced set of manually classified galaxies. Results are evaluated for accuracy by comparison to the annotation of Pan-STARRS included in a previous broad morphology catalog of SDSS galaxies. Our analysis shows that a CNN combined with several filters is an effective approach for annotating the galaxies and removing unclean images. The catalog contains morphology labels for 1,662,190 galaxies with ~95% accuracy. The accuracy can be further improved by selecting labels above certain confidence thresholds. The catalog is publicly available.
