Detection of Periodicity Based on Serial Dependence of Phase-Folded Data

TL;DR

This paper presents new non-parametric periodicity detection methods for unevenly-spaced sparse data, demonstrating that the Hoeffding-test metric outperforms classical methods in certain astronomical scenarios.

Contribution

Introduces and evaluates novel serial dependence-based periodicity metrics, highlighting the superior performance of the Hoeffding-test metric in specific conditions.

Findings

Hoeffding-test metric outperforms classical methods in simulations

Performance varies with signal shape, data points, and noise levels

Suggests using the Hoeffding-test metric alongside traditional methods

Abstract

We introduce and test several novel approaches for periodicity detection in unevenly-spaced sparse datasets. Specifically, we examine five different kinds of periodicity metrics, which are based on non-parametric measures of serial dependence of the phase-folded data. We test the metrics through simulations in which we assess their performance in various situations, including various periodic signal shapes, different numbers of data points and different signal to noise ratios. One of the periodicity metrics we introduce seems to perform significantly better than the classical ones in some settings of interest to astronomers. We suggest that this periodicity metric - the Hoeffding-test periodicity metric - should be used in addition to the traditional methods, to increase periodicity detection probability.