论文标题
通过机器学习区分静态和尘土飞扬的星系的一种方法
A Method to Distinguish Quiescent and Dusty Star-forming Galaxies with Machine Learning
论文作者
论文摘要
大型光度测量提供了丰富的静态星系观测来源，其中包括Z> 1的令人惊讶的人口。但是，由于它们与尘土飞扬的星系星系等闯入者的近分化，因此很难识别大量但干净的静态星系样本被证明很困难。我们描述了一种基于T分布的随机邻居嵌入（T-SNE）选择静态星系的新技术，这是一种无监督的机器学习算法，用于降低维度。这种T-SNE选择既具有比UVJ的改进，否则可以通过颜色选择，并且对光度模板拟合，更强烈地朝着高红移。由于在我们的假设下，高红和低速静止星系的颜色之间的相似性，T-SNE优于模板在RedShifts的63％的试验中拟合了模板，在那里，在RedShifts的试验中，大型训练样本已经存在。它也可能能够在更高的红移下比训练样本更有效地选择静态星系。
Large photometric surveys provide a rich source of observations of quiescent galaxies, including a surprisingly large population at z>1. However, identifying large, but clean, samples of quiescent galaxies has proven difficult because of their near-degeneracy with interlopers such as dusty, star-forming galaxies. We describe a new technique for selecting quiescent galaxies based upon t-distributed stochastic neighbor embedding (t-SNE), an unsupervised machine learning algorithm for dimensionality reduction. This t-SNE selection provides an improvement both over UVJ, removing interlopers which otherwise would pass color selection, and over photometric template fitting, more strongly towards high redshift. Due to the similarity between the colors of high- and low-redshift quiescent galaxies, under our assumptions t-SNE outperforms template fitting in 63% of trials at redshifts where a large training sample already exists. It also may be able to select quiescent galaxies more efficiently at higher redshifts than the training sample.
