Detection of Periodicity Based on Independence Tests - III. Phase Distance Correlation Periodogram

TL;DR

The paper introduces the Phase Distance Correlation periodogram, a new periodicity detection method based on distance correlation of phases, which excels with sparse and sawtooth-like data, outperforming traditional methods in specific cases.

Contribution

It adapts the distance correlation concept to circular variables, creating a novel periodicity metric suited for sparse datasets and challenging periodic signals.

Findings

Performs better than other methods for sawtooth-like periodicities.

Almost as effective as Lomb-Scargle in other contexts.

Potential for adaptation to astrometric and large datasets.

Abstract

I present the Phase Distance Correlation (PDC) periodogram -- a new periodicity metric, based on the Distance Correlation concept of G\'abor Sz\'ekely. For each trial period PDC calculates the distance correlation between the data samples and their phases. PDC requires adaptation of the Sz\'ekely's distance correlation to circular variables (phases). The resulting periodicity metric is best suited to sparse datasets, and it performs better than other methods for sawtooth-like periodicities. These include Cepheid and RR-Lyrae light curves, as well as radial velocity curves of eccentric spectroscopic binaries. The performance of the PDC periodogram in other contexts is almost as good as that of the Generalized Lomb-Scargle periodogram. The concept of phase distance correlation can be adapted also to astrometric data, and it has the potential to be suitable also for large evenly-spaced datasets, after some algorithmic perfection.