Period Estimation in Astronomical Time Series Using Slotted Correntropy

TL;DR

This paper introduces a novel period estimation method for astronomical light curves using slotted correntropy, effectively handling noisy and irregular data to improve accuracy over existing techniques.

Contribution

The paper presents a new slotted correntropy approach with an information theoretic metric for better period detection in unevenly sampled astronomical time series.

Findings

Outperforms traditional methods like Lomb-Scargle and SigSpec

Effective on noisy, irregularly sampled light curves

Demonstrated success on MACHO survey data

Abstract

In this letter, we propose a method for period estimation in light curves from periodic variable stars using correntropy. Light curves are astronomical time series of stellar brightness over time, and are characterized as being noisy and unevenly sampled. We propose to use slotted time lags in order to estimate correntropy directly from irregularly sampled time series. A new information theoretic metric is proposed for discriminating among the peaks of the correntropy spectral density. The slotted correntropy method outperformed slotted correlation, string length, VarTools (Lomb-Scargle periodogram and Analysis of Variance), and SigSpec applications on a set of light curves drawn from the MACHO survey.