Joint Modeling of Quasar Variability and Accretion Disk Reprocessing using Latent Stochastic Differential Equations

TL;DR

This paper introduces a novel deep learning framework that jointly models quasar variability and accretion disk reprocessing using latent stochastic differential equations, enabling better parameter inference from simulated light curves.

Contribution

It presents the first auto-differentiable simulation of accretion disk reprocessing integrated into a neural network for joint modeling of quasar variability.

Findings

Outperforms Gaussian process regression baseline.

Accurately infers accretion disk parameters and time delays.

Effective even with out-of-distribution driving signals.

Abstract

Quasars are bright active galactic nuclei powered by the accretion of matter around supermassive black holes at the center of galaxies. Their stochastic brightness variability depends on the physical properties of the accretion disk and black hole. The upcoming Rubin Observatory Legacy Survey of Space and Time (LSST) is expected to observe tens of millions of quasars, so there is a need for efficient techniques like machine learning that can handle the large volume of data. Quasar variability is believed to be driven by an X-ray corona, which is reprocessed by the accretion disk and emitted as UV/optical variability. We are the first to introduce an auto-differentiable simulation of the accretion disk and reprocessing. We use the simulation as a direct component of our neural network to jointly model the driving variability and reprocessing, trained with supervised learning on simulated LSST-like 10 yr quasar light curves. We encode the light curves using a transformer encoder, and the driving variability is reconstructed using latent stochastic differential equations, a physically motivated generative deep learning method that can model continuous-time stochastic dynamics. By embedding the physical processes of the driving signal and reprocessing into our network, we achieve a model that is more robust and interpretable. We demonstrate that our model outperforms a Gaussian process regression baseline and can infer accretion disk parameters and time delays between wave bands, even for out-of-distribution driving signals. Our approach provides a powerful framework that can be adapted to solve other inverse problems in multivariate time series.