Distortions in Periodicity Analysis of Blazars II: The Impact of Gaps

TL;DR

This study examines how observational gaps in blazar time series data affect variability analysis, revealing that gaps over 50% significantly impair period detection and can cause false signals, with SSA showing relative robustness.

Contribution

It systematically evaluates the impact of data gaps on common time series methods for blazar variability, highlighting SSA's resilience and identifying seasonal sampling effects.

Findings

Gaps over 50% degrade period detection reliability.

SSA method remains robust despite high data incompleteness.

Seasonal sampling effects can cause false periodicity detections.

Abstract

Time series analysis is fundamental to characterizing the variability inherent in multi-wavelength emissions from blazars. However, a major observational challenge lies in the need for well-sampled, temporally uniform data, which is often hindered by irregular sampling and data gaps. These gaps can significantly affect the reliability and accuracy of methods used to probe source variability. This paper investigates the impact of such observational gaps on time series analysis of blazar emissions. To do so, we systematically evaluate how these gaps alter observed variability patterns, mask genuine periodic signals, and introduce false periodicity detections. This evaluation is conducted using both simulated and real observational data. We assess a range of widely used time series analysis methods, including the Lomb-Scargle periodogram, Phase Dispersion Minimization, and the recently proposed Singular Spectrum Analysis (SSA). Our results demonstrate a clear and significant degradation in period detection reliability when the percentage of gaps exceeds 50\%. In such cases, the period-significance relationship becomes increasingly distorted, often leading to misleading results. Among the tested methods, SSA stands out for its ability to yield consistent and robust detections despite high data incompleteness. Additionally, the analyzed methods tend to identify artificial periodicities of around one year, likely due to seasonal sampling effects, which can result in false positives if not carefully recognized. Finally, the periods detected with $\geq$3$\sigma$ confidence are unlikely to result from stochastic processes or from the presence of gaps in the analyzed time series.