Assigning degrees of stochasticity to blazar light curves in the radio band using complex networks

TL;DR

This study uses complex network analysis, specifically Horizontal Visibility Graphs, to characterize the stochasticity of blazar radio light curves, revealing differences related to spectral types and proposing a new method for AGN variability analysis.

Contribution

It introduces the application of HVG-based degree distribution analysis and Kullback-Leibler Divergence to classify blazar variability, offering a novel perspective beyond traditional measures.

Findings

Degree distributions detect differences related to spectral classification.

BL Lac sources exhibit chaotic behavior in their light curves.

FSRQ sources show correlated stochastic behavior.

Abstract

{We focus on characterizing the high-energy emission mechanisms of blazars by analyzing the variability in the radio band of the light curves of more than a thousand sources. We are interested in assigning complexity parameters to these sources, modeling the time series of the light curves with the method of the Horizontal Visibility Graph (HVG), which allows us to obtain properties from degree distributions, such as a characteristic exponent to describe its stochasticity and the Kullback-Leibler Divergence (KLD), presenting a new perspective to the methods commonly used to study Active Galactic Nuclei (AGN). We contrast these parameters with the excess variance, an astronomical measurement of variability in light curves, at the same time we use the spectral classification of the sources. While it is not possible to find significant correlations with the excess variance, the degree distributions extracted from the network are detecting differences related to the spectral classification of blazars. These differences suggest a chaotic behavior in the time series for the BL Lac sources and a correlated stochastic behavior in the time series for the FSRQ sources. Our results show that complex networks may be a valuable alternative tool to study AGNs according to the variability of their energy output.