基于信息熵的变星光谱快速识别方法

变星对人类研究宇宙的起源与发展具有重要意义,对于变星研究的困难首先源于对变星的筛选和识别,即如何从海量恒星光谱数据中有效识别变星光谱。传统的异常数据定义试图通过不同的方式寻找异常数据与一般模式之间的偏差,进而予以定量分析和筛选。然而,这种方法的时间复杂度过大,且结果存在不可理解和无法解释的问题。文章利用熵可以反映系统有序程度与稳定程度的特性,引入信息熵作为衡量数据集一般模式的标准,提出了基于信息熵的变星光谱快速识别方法。该方法显著降低了算法的时间复杂度,有效地消除了人为主观因素对识别结果的影响。采用国家天文台提供的Sloan数字巡天数据实验验证了该方法的可行性和有效性。

Rapid identification of variable star spectrum based on information entropy

Variable star is very important for mankind studying cosmic origin and evolution. For studying variable star, the chief difficulty results from the filtration and identification of variable star, that is how to validly identify variable star spectra from large high-dimensional star spectra data. The traditional outlier definition tries to find the difference between the outlier data and the general model by different ways, and then the result is quantitatively analyzed and filtrated. However, the time complexity of this method is over size and its results are inscrutable and unaccountable. Information entropy is a measure of the uncertainty associated with a random variable. In the present paper, information entropy is imported as the standard of dataset common mode. A novel method is proposed to identify the variable star spectrum rapidly based on information entropy. The time complexity of this method is observably reduced and the man-made impact is effectively overcome. The preliminary experimental results based on Sloan star spectrum data show that the method is workable for rapid identification of variable star spectrum.