A model for AGN variability on multiple timescales

TL;DR

This paper introduces a comprehensive model linking AGN variability across a vast range of timescales, from days to billions of years, based on statistical properties of black hole accretion rates.

Contribution

It proposes a unified framework and model that explain diverse AGN variability features using a single or limited set of Eddington ratio distributions and power spectral densities.

Findings

The model can simulate AGN light curves across multiple timescales.

Variability measurements from days to Gyr are consistent with the proposed framework.

Constraints on the underlying PSD provide insights into black hole growth processes.

Abstract

We present a framework to link and describe AGN variability on a wide range of timescales, from days to billions of years. In particular, we concentrate on the AGN variability features related to changes in black hole fuelling and accretion rate. In our framework, the variability features observed in different AGN at different timescales may be explained as realisations of the same underlying statistical properties. In this context, we propose a model to simulate the evolution of AGN light curves with time based on the probability density function (PDF) and power spectral density (PSD) of the Eddington ratio ($L/L_{\rm Edd}$) distribution. Motivated by general galaxy population properties, we propose that the PDF may be inspired by the $L/L_{\rm Edd}$ distribution function (ERDF), and that a single (or limited number of) ERDF+PSD set may explain all observed variability features. After outlining the framework and the model, we compile a set of variability measurements in terms of structure function (SF) and magnitude difference. We then combine the variability measurements on a SF plot ranging from days to Gyr. The proposed framework enables constraints on the underlying PSD and the ability to link AGN variability on different timescales, therefore providing new insights into AGN variability and black hole growth phenomena.