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arXiv · 1307.2209

A comparison of period finding algorithms

Matthew J. Graham·Andrew J. Drake·S. G. Djorgovski·Ashish A. Mahabal·Ciro Donalek·Victor Duan·Alison Maher
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Abstract

This paper presents a comparison of popular period finding algorithms applied to the light curves of variable stars from the Catalina Real-time Transient Survey (CRTS), MACHO and ASAS data sets. We analyze the accuracy of the methods against magnitude, sampling rates, quoted period, quality measures (signal-to-noise and number of observations), variability, and object classes. We find that measure of dispersion-based techniques - analysis-of-variance with harmonics and conditional entropy - consistently give the best results but there are clear dependencies on object class and light curve quality. Period aliasing and identifying a period harmonic also remain significant issues. We consider the performance of the algorithms and show that a new conditional entropy-based algorithm is the most optimal in terms of completeness and speed. We also consider a simple ensemble approach and find that it performs no better than individual algorithms.

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Matthew J. Graham, Andrew J. Drake, S. G. Djorgovski, Ashish A. Mahabal, Ciro Donalek, Victor Duan, Alison Maher. 2013-07-08. A comparison of period finding algorithms. https://doi.org/10.1093/mnras/stt1264

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