On the Detection and Characterization of Quasiperiodic Oscillations in Astronomical Time Series: Gamma-Ray Burst X-Ray Light Curves as a Test Case

TL;DR

This paper compares three methods—Gaussian process, power spectrum, and wavelet analysis—for detecting quasiperiodic signals in astronomical time series, specifically analyzing gamma-ray burst X-ray light curves to identify artifacts and improve variability studies.

Contribution

It provides a critical comparison of three analytical approaches for quasiperiodic detection in astronomical data, highlighting artifacts and methodological considerations.

Findings

False quasiperiodic signals can arise from preprocessing artifacts.

Wavelet analysis can help identify transient quasiperiodic features.

Method comparison informs better analysis of astronomical variability.

Abstract

The study of temporal properties of variable sources can elucidate their physical processes. In this context, we present a critical study comparing three approaches to periodic or quasiperiodic behavior: Gaussian process, power spectrum, and wavelet analysis, using celerite, Lomb-Scargle periodograms, and weighted wavelet-Z transforms, respectively. We use 15 Swift-X-ray Telescope light curves of short gamma-ray bursts (sGRBs) as examples. A comprehensive analysis of two sGRB X-ray light curves is performed. The results reveal the importance of artifacts, largely in the form of false quasiperiodic oscillation signals, possibly introduced by preprocessing (such as detrending) or other aspects of the analysis. The exploration described in this paper can be helpful for future studies of variability in GRBs, active galactic nuclei, and other astronomical sources.