Extracting the X-ray reverberation response functions from the AGN light curves using an autoencoder
Sanhanat Deesamutara, Poemwai Chainakun, Tirawut Worrakitpoonpon, Kamonwan Khanthasombat, Wasutep Luangtip, Jiachen Jiang, Francisco Pozo Nu\~nez, Andrew J. Young

TL;DR
This paper introduces a machine learning approach using a variational autoencoder to directly extract X-ray reverberation response functions from AGN light curves, enabling new insights into coronal geometry.
Contribution
The study demonstrates the first direct estimation of AGN response functions from observational data using a VAE, revealing coronal height variations correlated with luminosity.
Findings
VAE successfully recognizes reverberation patterns in simulated light curves.
Applied to IRAS 13224-3809, the VAE estimates coronal height changes between 3 and 20 gravitational radii.
Coronal height variations are correlated with source luminosity, consistent with previous studies.
Abstract
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 lamp-post 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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
