2025ApJ...980..257D

We study the X-ray reverberation in active galactic nuclei (AGN) using the variational autoencoder (VAE), which is a machine learning algorithm widely used for signal processing and feature reconstruction. While the X-ray reverberation signatures that contain the information of the accretion disk and the X-ray-emitting corona are commonly analyzed in the Fourier domain, this work aims to extract the reverberation response functions directly from the AGN light curves. The VAE is trained using the simulated light curves that contain the primary X-rays from the lamppost corona, varying its height and the corresponding reflection X-rays from the disk. We use progressively more realistic light-curve models, such as those that include the effects of disk-propagating fluctuations and random noises, to assess the ability of the VAE to reconstruct the response profiles. Interestingly, the VAE can recognize the reverberation patterns on the light curves; hence, the coronal height can be predicted. We then deploy the VAE model on the XMM-Newton data of IRAS 13224-3809 and directly estimate, for the first time, the response functions of this source in various observations. The result reveals the corona changing its height between 3r_g and 20r_g, which is correlated with the source luminosity and in line with previous literature. Finally, we discuss the advantages and limitations of this method.