从FATS到英尺：基于机器学习的天文特征提取工具的进一步改进
From FATS to feets: Further improvements to an astronomical feature extraction tool based on machine learning
摘要：
机器学习算法对于在这个peta级公共调查数据发布时代对天文学中的时间序列数据进行分类非常有用。这些方法可以促进在大多数天体物理学领域发现新的未知事件，并改进对已知现象样本的分析。机器学习算法使用从收集的数据中提取的特征作为输入预测变量。一个名为时间序列特征分析（FATS）的公共工具已被证明是特征提取的优秀工具，特别是可变对象的光曲线分类。在这项研究中，我们提出了一个重大的改进，FATS，纠正不方便的设计选择，小细节，和文件的重新设计过程。这一改进包括一个名为“feets”的新Python包，这对天文软件工具的未来代码重构非常重要。
英文摘要：
Machine learning algorithms are highly useful for the classification of time series data in astronomy in this era of peta-scale public survey data releases. These methods can facilitate the discovery of new unknown events in most astrophysical areas, as well as improving the analysis of samples of known phenomena. Machine learning algorithms use features extracted from collected data as input predictive variables. A public tool called Feature Analysis for Time Series (FATS) has proved an excellent workhorse for feature extraction, particularly light curve classification for variable objects. In this study, we present a major improvement to FATS, which corrects inconvenient design choices, minor details, and documentation for the re-engineering process. This improvement comprises a new Python package called "feets", which is important for future code-refactoring for astronomical software tools.
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