Using conditional entropy to identify periodicity
Using conditional entropy to identify periodicity
复制标题
使用条件熵来识别周期性
DOI：
10.1093/mnras/stt1206
复制
发表时间：
2013
影响因子：
4.8
通讯作者：
C. Donalek
中科院分区：
文献类型：
--
作者：
M. Graham;A. Drake;S. Djorgovski;A. Mahabal;C. Donalek
本文提出了一种基于条件熵的新的周期查找方法，该方法既高效又准确。我们证明了它在模拟和真实数据上的适用性。我们发现它的性能与使用模拟数据的其他基于信息的技术相当，但在查找周期和识别周期性行为方面都优于真实数据。特别是，它对于其他周期查找算法发现的常见混叠问题具有很强的鲁棒性。
This paper presents a new period-finding method based on conditional entropy that is both efficient and accurate. We demonstrate its applicability on simulated and real data. We find that it has comparable performance to other information-based techniques with simulated data but is superior with real data, both for finding periods and for just identifying periodic behaviour. In particular, it is robust against common aliasing issues found with other period-finding algorithms.