论文标题
星系分类：关于Otelo和Cosmos数据库的深度学习
Galaxy classification: deep learning on the OTELO and COSMOS databases
论文作者
论文摘要
语境。如果我们想了解宇宙及其进化，必须在现代深度调查中观察到数十万个星系的准确分类。目标。在这里，我们报告了使用机器学习技术对Otelo和Cosmos数据库中的早期和晚期星系进行分类，并使用光学和红外光度法以及可用的形状参数：刻度指数或浓度指数。方法。我们为Otelo数据库使用了三种分类方法：1）U-R颜色分离，2）使用U-R和形状参数分类的线性判别分析，以及3）使用R幅度，几种颜色和形状参数的深神经网络。我们通过示例引导程序分析了每种方法的性能，并使用COSMOS数据测试了神经网络体系结构的性能。结果。深神经网络所达到的准确性大于其他分类方法的准确性，并且它也可以在缺少数据的情况下运行。我们的神经网络体系结构能够对Otelo和Cosmos数据集进行分类，而不管每个目录中使用的光度带的较小差异。结论。在这项研究中，我们表明，深度神经网络的使用是挖掘分类数据的强大方法
Context. The accurate classification of hundreds of thousands of galaxies observed in modern deep surveys is imperative if we want to understand the universe and its evolution. Aims. Here, we report the use of machine learning techniques to classify early- and late-type galaxies in the OTELO and COSMOS databases using optical and infrared photometry and available shape parameters: either the Sersic index or the concentration index. Methods. We used three classification methods for the OTELO database: 1) u-r color separation , 2) linear discriminant analysis using u-r and a shape parameter classification, and 3) a deep neural network using the r magnitude, several colors, and a shape parameter. We analyzed the performance of each method by sample bootstrapping and tested the performance of our neural network architecture using COSMOS data. Results. The accuracy achieved by the deep neural network is greater than that of the other classification methods, and it can also operate with missing data. Our neural network architecture is able to classify both OTELO and COSMOS datasets regardless of small differences in the photometric bands used in each catalog. Conclusions. In this study we show that the use of deep neural networks is a robust method to mine the cataloged data
