Extracting Information from AGN Variability

TL;DR

This paper introduces a Green's Function-based method to analyze AGN variability, modeling light curves as driven linear differential equations, revealing characteristic timescales and flux perturbation behaviors.

Contribution

It presents a novel Green's Function approach for extracting timescales and driving mechanisms from AGN light curves, demonstrated on Kepler data.

Findings

Identified a 385-day damping timescale in AGN variability.

Revealed flux perturbation growth and decay timescales of 5.6 and 67 days.

Provided an open-source software package KALI for community use.

Abstract

AGN exhibit rapid, high amplitude stochastic flux variations across the entire electromagnetic spectrum on timescales ranging from hours to years. The cause of this variability is poorly understood. We present a Green's Function-based method for using variability to (1) measure the time-scales on which flux perturbations evolve and (2) characterize the driving flux perturbations. We model the observed light curve of an AGN as a linear differential equation driven by stochastic impulses. We analyze the light curve of the Kepler AGN Zw 229-15 and find that the observed variability behavior can be modeled as a damped harmonic oscillator perturbed by a colored noise process. The model powerspectrum turns over on time-scale $385$~d. On shorter time-scales, the log-powerspectrum slope varies between $2$ and $4$, explaining the behavior noted by previous studies. We recover and identify both the $5.6$~d and $67$~d timescales reported by previous work using the Green's Function of the C-ARMA equation rather than by directly fitting the powerspectrum of the light curve. These are the timescales on which flux perturbations grow, and on which flux perturbations decay back to the steady-state flux level respectively. We make the software package KALI used to study light curves using our method available to the community.