基于深度神经网络算法研究类星体光谱中的谱线特征

活动时间： 12月11日15时00分       

地            点  ： 理科2号楼A302

讲座内容：

Metal absorption line systems in the distant quasar spectra have been used as one of the most powerful tools to probe gas content in the early Universe. The MgII λλ 2796, 2803 doublet is one of the most popular metal absorption lines and has been used to trace gas and global star formation at redshifts between ~0.5 to 2.5. Machine learning algorithms have been used to detect absorption lines systems in the large sky survey, such as Principle Component Analysis, Gaussian Process and decision tree, but the overall detection process is not only complicated, but also time consuming. It usually takes a few months to go through the entire quasar spectral dataset from each of the Sloan Digital Sky Survey (SDSS) data release. In this work, we applied the deep neural network, or “ deep learning” algorithms, in the most recently SDSS DR14 quasar spectra and were able to randomly search 20000 quasar spectra and detect 2887 strong Mg II absorption features in just 9 seconds. Our detection algorithms were verified with previously released DR12 and DR7 data and published Mg II catalog and the detection accuracy is 90 %. This is the first time that deep neural network has demonstrated its promising power in both speed and accuracy in replacing tedious, repetitive human work in searching for narrow absorption patterns in a big dataset. I will present our detection algorithm in this talk.

主讲人介绍：

赵一楠，美国佛罗里达大学天文学系博士研究生，主要研究领域包括星际介质、星系际介质、伽玛暴射线暴、活动星系核和机器学习及相关计算机科学应用。 2014年在河北师范大学物理科学与信息工程学院本科毕业后，开始在美国佛罗里达大学天文学系直接攻读博士学位至今。在师大本科学习期间即在The Astrophysical Journal (ApJ: 1区TOP期刊，当年SCI影响因子6.7)上发表第一作者学术论文1篇， 目前已发表SCI论文4篇（包括2篇ApJ，2篇MNRAS）和IEEE计算机科学论文1篇。

发布时间：2017-12-07 14:02:03