Abstract
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 ~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 ~0.0024 pc.
Highlights
Active galactic nuclei (AGNs) vary on time-scales ranging from minutes and hours to years over the entire electromagnetic spectrum, with no apparent indications of periodicities
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 active galactic nuclei (AGN) time series and newly published observed time series of changing look AGN (CL AGN) NGC 3516
Our hybrid method based on two-dimensional (2D) correlation analysis were devised to deal with above issues (Kovačević et al 2018)
Summary
Active galactic nuclei (AGNs) vary on time-scales ranging from minutes and hours to years over the entire electromagnetic spectrum, with no apparent indications of periodicities. Many methods have been designed for estimating this periodicity (for an excellent review see Graham et al.2013). These methods share a number of commonalities, as well as differences. Wavelet analysis does not assume stationarity and is able to detect amplitude and period changes over time. In all Fourier based methods peaks which are indicating periodicity can overlap. The Fourier transform, the wavelet transform and related period estimation techniques can not tell about the presence of coordinated or independent changes among signals, as well as about relative directions of signal intensity variations. Our hybrid method based on two-dimensional (2D) correlation analysis were devised to deal with above issues (Kovačević et al 2018)
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