Two-dimensional correlation analysis of periodicity in active galactic nuclei time series

TL;DR

This paper introduces a new 2D correlation method combined with Gaussian processes to objectively detect periodicities in AGN time series, overcoming challenges posed by their complex, noisy variability.

Contribution

The paper presents a novel analytical technique that effectively identifies periodic signals in AGN data, demonstrated on artificial and real AGN time series.

Findings

Successfully detected a ~4-year periodicity in NGC 3516

Method distinguishes oscillatory signals from noise in complex time series

Potential link between detected periodicity and accretion disc instability

Abstract

The active galactic nuclei (AGN) are among the most powerful sources with an inherent, pronounced and random variation of brightness. The randomness of their time series is so subtle as to blur the border between aperiodic fluctuations and noisy oscillations. This poses challenges to analysing of such time series because neither visual inspection nor pre-exisitng methods can identify well oscillatory signals in them. Thus, there is a need for an objective method for periodicity detection. Here we review our a new data analysis method that combines a two-dimensional correlation (2D) of time series with the powerful methods of Gaussian processes. To demonstrate the utility of this technique, we apply it to two example problems which were not exploited enough: damped rednoised artificial time series mimicking AGN time series and newly published observed time series of changing look AGN (CL AGN) NGC 3516. The method successfully detected periodicities in both types of time series. Identified periodicity of $\sim 4$ yr in NGC 3516 allows us to speculate that if the thermal instability formed in its accretion disc (AD) on a time scale resembling detected periodicity then AD radius could be $\sim 0.0024$ pc.