Long-term radio variability of active galactic nuclei at 37 GHz

TL;DR

This study analyzes 42 years of 37 GHz radio data from active galactic nuclei to identify characteristic variability timescales, revealing that long-term monitoring is often necessary to capture their complex behavior and linking timescales to jet structures.

Contribution

It introduces a method to constrain variability timescales in AGN radio emission using periodogram analysis and compares these timescales with VLBI jet features.

Findings

Constrained characteristic timescales for 11 sources with an average of 1300 days.

Found that 42 years of data may still be insufficient to fully characterize variability.

Suggested a connection between variability timescales and jet structure based on VLBI observations.

Abstract

We present the results of analysing the long-term radio variability of active galactic nuclei at 37 GHz using data of 123 sources observed in the Aalto University Mets\"ahovi Radio Observatory. Our aim was to constrain the characteristic timescales of the studied sources and to analyse whether up to 42 years of monitoring was enough to describe their variability behaviour. We used a periodogram to estimate the power spectral density of each source. The power spectral density is used to analyse the power content of a time series in the frequency domain, and it is a powerful tool in describing the variability of active galactic nuclei. We were interested in finding a bend frequency in the power spectrum, that is, a frequency at which the slope $\beta$ of the spectrum changes from a non-zero value to zero. We fitted two models to the periodograms of each source, namely the bending power law and the simple power law. The bend frequency in the bending power law corresponds to a characteristic timescale. We were able to constrain a timescale for 11 out of 123 sources, with an average characteristic timescale x_b = 1300 days and an average power-law slope $\beta$ = 2.3. The results suggest that up to 42 years of observations may not always be enough for obtaining a characteristic timescale in the radio domain. This is likely caused by a combination of both slow variability as well as sampling induced effects. We also compared the obtained timescales to 43 GHz very long baseline interferometry images. The maximum length of time a knot was visible was often close to the obtained characteristic timescale. This suggests a connection between the characteristic timescale and the jet structure.