An Information Theoretic Algorithm for Finding Periodicities in Stellar Light Curves

TL;DR

This paper introduces a novel information theoretic metric called CKP for detecting periodicities in noisy, unevenly sampled stellar light curves, improving accuracy over traditional methods.

Contribution

The paper presents the Correntropy Kernelized Periodogram (CKP), a new direct computation method for periodicity detection that outperforms existing techniques in astronomical data analysis.

Findings

Achieved 97.2% true positives in periodicity discrimination.

Attained 88% accuracy in period estimation with low false positives.

Outperformed previous methods like slotted correntropy in tests.

Abstract

We propose a new information theoretic metric for finding periodicities in stellar light curves. Light curves are astronomical time series of brightness over time, and are characterized as being noisy and unevenly sampled. The proposed metric combines correntropy (generalized correlation) with a periodic kernel to measure similarity among samples separated by a given period. The new metric provides a periodogram, called Correntropy Kernelized Periodogram (CKP), whose peaks are associated with the fundamental frequencies present in the data. The CKP does not require any resampling, slotting or folding scheme as it is computed directly from the available samples. CKP is the main part of a fully-automated pipeline for periodic light curve discrimination to be used in astronomical survey databases. We show that the CKP method outperformed the slotted correntropy, and conventional methods used in astronomy for periodicity discrimination and period estimation tasks, using a set of light curves drawn from the MACHO survey. The proposed metric achieved 97.2% of true positives with 0% of false positives at the confidence level of 99% for the periodicity discrimination task; and 88% of hits with 11.6% of multiples and 0.4% of misses in the period estimation task.