Period Estimation in Astronomical Time Series Using Slotted Correntropy

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.

References in corpus (3)

• Automated supervised classification of variable stars I. Methodology
• SigSpec - I. Frequency- and Phase-Resolved Significance in Fourier Space
• Deep MMT Transit Survey of the Open Cluster M37 II: Variable Stars

Cited by in corpus (11)

• Understanding the Lomb-Scargle Periodogram
• A comparison of period finding algorithms
• Using conditional entropy to identify periodicity
• An Information Theoretic Algorithm for Finding Periodicities in Stellar Light Curves
• Nonparametric Bayesian Estimation of Periodic Functions
• An Automated tool to detect variable sources in the Vista Variables in the V\'ıa Láctea Survey. The VVV Variables (V${}^4$) catalog of tiles d001 and d002
• Near-infrared photometry and spectroscopy of the low Galactic latitude globular cluster 2MASS-GC03
• New Insights into Time Series Analysis III - Setting constraints on period analysis
• Variable Star Signature Classification using Slotted Symbolic Markov Modeling
• The G 305 Star-forming Region: II. Irregular variable stars
• Advanced Astroinformatics for Variable Star Classification