基于时隙符号马尔可夫模型的变星签名分类
Variable Star Signature Classification using Slotted Symbolic Markov Modeling
摘要：
随着数字天文学的出现，新的好处和新的挑战已经呈现给现代天文学家。天文学家不能再依赖人工处理，而整个行业已开始采用更先进的计算手段。本文重点研究了一种新的时域特征提取方法的构建和应用，以及一种支持监督模式分类算法的发展，用于变星的识别。介绍了一种将恒星变量观测（时域数据）减少到一个新的特征空间表示的方法。所提出的方法将被称为时隙符号马尔可夫模型（SSMM），并具有许多优点，这将被证明是有益的，特别是监督分类的恒星变量。它将表明，该方法优于一个标准的恒星光变曲线数据集的基线标准方法。还将显示从LINEAR数据集导出的一组数据的性能
英文摘要：
With the advent of digital astronomy, new benefits and new challenges have been presented to the modern day astronomer. No longer can the astronomer rely on manual processing, instead the profession as a whole has begun to adopt more advanced computational means. This paper focuses on the construction and application of a novel time-domain signature extraction methodology and the development of a supporting supervised pattern classification algorithm for the identification of variable stars. A methodology for the reduction of stellar variable observations (time-domain data) into a novel feature space representation is introduced. The methodology presented will be referred to as Slotted Symbolic Markov Modeling (SSMM) and has a number of advantages which will be demonstrated to be beneficial; specifically to the supervised classification of stellar variables. It will be shown that the methodology outperformed a baseline standard methodology on a standardized set of stellar light curve data. The performance on a set of data derived from the LINEAR dataset will also be shown
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