A forward modelling approach to AGN variability -- Method description and early applications

TL;DR

This paper introduces a GPU-accelerated numerical framework for simulating AGN variability across various timescales, aiding the analysis and interpretation of data from current and future time domain surveys like LSST.

Contribution

The authors develop a novel GPU-based simulation framework that models AGN variability, linking it to population properties and enabling long-term light curve generation for survey planning.

Findings

Successfully reproduces observed AGN variability in PTF/iPTF survey

Generates long light curves over 1 million years with weekly cadence

Framework is ready for application to upcoming surveys like LSST

Abstract

We present a numerical framework for the variability of active galactic nuclei (AGN), which links the variability of AGN over a broad range of timescales and luminosities to the observed properties of the AGN population as a whole, and particularly the Eddington ratio distribution function (ERDF). We have implemented our framework on GPU architecture, relying on previously published time series generating algorithms. After extensive tests that characterise several intrinsic and numerical aspects of the simulations, we describe some applications used for current and future time domain surveys and for the study of extremely variable sources (e.g., "changing look" or flaring AGN). Specifically, we define a simulation setup which reproduces the AGN variability observed in the PTF/iPTF survey, and use it to forward model longer light curves of the kind that may be observed within the LSST main survey. Thanks to our effcient implementations, these simulations are able to cover for example over 1 Myr with a roughly weekly cadence. We envision that this framework will become highly valuable to prepare for, and best exploit, data from upcoming time domain surveys, such as for example LSST.