A comparison of period finding algorithms

TL;DR

This paper compares various period finding algorithms for variable star light curves, highlighting the superior performance of dispersion-based methods and introducing a new entropy-based algorithm.

Contribution

It provides a comprehensive comparison of algorithms and introduces a new, more effective entropy-based period finding method.

Findings

Analysis-of-variance with harmonics performs best overall.

The new conditional entropy-based algorithm is the most optimal.

Ensemble methods do not outperform individual algorithms.

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.