A comparison of period finding algorithms

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, MACHO and ASAS data sets. We analyse 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 dependences 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.

• Publication year

  2013

• Venue

  Monthly Notices of the Royal Astronomical Society

• Publication date

  2013-07-08

• Fields of study

  Physics

• Identifiers
  DOI 10.1093/mnras/stt1264arXiv 1307.2209
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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