天文光变周期提取算法综述

发布时间：2018-11-06 12:13

【摘要】：随着大量巡天项目的开展和完成,天文领域积累了大量的光变数据。这些数据中蕴含了许多有重要价值的物理参数,如光变周期,它对研究光变的物理机制、爆发预测和天体质量估算等有很大的帮助。但受到天体自身动力学过程、观测条件和仪器状态等客观因素的影响,光变数据往往是非均匀采样的,并且都不同程度地受到噪声的影响。因此,非常有必要研究基于非均匀采样信号的光变周期提取算法。综述了基于频域、时域和时-频域分析的三大类光变周期提取算法,主要包括Lomb-Scargle周期图法、Jurkevich算法和加权小波Z变换算法等,着重分析和比较了这些算法的性能,同时分析了它们的优缺点,最后总结与展望了光变周期算法。
[Abstract]:With the development and completion of a large number of survey projects, a large number of photovoltaic data have been accumulated in the field of astronomy. These data contain many valuable physical parameters, such as the period of light variation, which is of great help to the study of the physical mechanism of light change, burst prediction and the estimation of celestial body quality. However, due to the objective factors such as the dynamic process of celestial bodies, observation conditions and instrument states, the photovoltaic data are often sampled inhomogeneously, and all of them are affected by noise to varying degrees. Therefore, it is necessary to study the optical period extraction algorithm based on nonuniform sampling signal. Based on frequency domain, time domain and time-frequency domain analysis, three kinds of optical variable period extraction algorithms are reviewed, including Lomb-Scargle periodic graph method, Jurkevich algorithm and weighted wavelet Z transform algorithm, etc. The performance of these algorithms is analyzed and compared emphatically. At the same time, the advantages and disadvantages of these algorithms are analyzed. Finally, the algorithm of optical period variation is summarized and prospected.
【作者单位】：中国科学院上海天文台;中国科学院射电天文重点开放实验室;桂林电子科技大学广西密码学组信息安全重点实验室;
【基金】：科技部政府间科技合作专项“SKA科学数据处理关键技术研究”;科技部973项目“SKA建设准备阶段关键问题研究”(2013CB837900)
【分类号】：P141

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