Related papers: Extracting the X-ray reverberation response functi…

The corona is an integral component of Active Galactic Nuclei (AGN) which produces the bulk of the X-ray emission above 1--2 keV. However, many of its physical properties and the mechanisms powering this emission remain a mystery. In…

We develop the power spectral density (PSD) model to explain the nature of the X-ray variability in IRAS 13224-3809, including the full effects of the X-ray reverberation due to the lamp-post source. We utilize 16 XMM-Newton observations…

Variational Autoencoders (VAE) are probabilistic deep generative models underpinned by elegant theory, stable training processes, and meaningful manifold representations. However, they produce blurry images due to a lack of explicit…

X-ray reverberation is a powerful technique which maps out the structure of the inner regions of accretion disks around black holes using the echoes of the coronal emission reflected by the disk. While the theory of X-ray reverberation has…

A thorough study of radio emission in Active Galactic Nuclei (AGN) is of fundamental importance to understand the physical mechanisms responsible for the emission and the interplay between accretion and ejection processes. High frequency…

The illumination of the accretion disks is frequently studied assuming that the incident X-ray flux is a point-like source. The approach is referred as lamppost model.The most recent computations of the X-ray reprocessing by the disk take…

Ultra-high-definition (UHD) image restoration often faces computational bottlenecks and information loss due to its extremely high resolution. Existing studies based on Variational Autoencoders (VAE) improve efficiency by transferring the…

In this paper we present a new implementation of a Variational Autoencoder (VAE) for the calibration of sensors. We propose that the VAE can be used to calibrate sensor data by training the latent space as a calibration output. We discuss…

In this paper, we demonstrate the potential of applying Variational Autoencoder (VAE) [10] for anomaly detection in skin disease images. VAE is a class of deep generative models which is trained by maximizing the evidence lower bound of…

It is widely accepted that X-ray emission in luminous active galactic nuclei (AGNs) originates from hot corona. To prevent the corona from over-cooling by strong X-ray emission, steady heating to the corona is essential, for which the most…

We introduce the variational graph auto-encoder (VGAE), a framework for unsupervised learning on graph-structured data based on the variational auto-encoder (VAE). This model makes use of latent variables and is capable of learning…

Correlated variability between coronal X-rays and disc optical/UV photons provides a very useful diagnostic of the interplay between the different regions around an active galactic nucleus (AGN) and how they interact. AGN that reveal strong…

Variational Autoencoders (VAEs) are powerful generative models capable of learning compact latent representations. However, conventional VAEs often generate relatively blurry images due to their assumption of an isotropic Gaussian latent…

I present a Variational Autoencoder (VAE) trained on collider physics data (specifically boosted $W$ jets), with reconstruction error given by an approximation to the Earth Movers Distance (EMD) between input and output jets. This VAE…

A thermal active galactic nucleus (AGN) consist of a powerful, broad-band continuum source that is surrounded by several reprocessing media with different geometries and compositions. Here we investigate the expected spectropolarimetric…

Normalizing flows, autoregressive models, variational autoencoders (VAEs), and deep energy-based models are among competing likelihood-based frameworks for deep generative learning. Among them, VAEs have the advantage of fast and tractable…

Among the wide variety of image generative models, two models stand out: Variational Auto Encoders (VAE) and Generative Adversarial Networks (GAN). GANs can produce realistic images, but they suffer from mode collapse and do not provide…

The Variational Autoencoder (VAE) is a powerful architecture capable of representation learning and generative modeling. When it comes to learning interpretable (disentangled) representations, VAE and its variants show unparalleled…

This paper demonstrates that a simple modification of the variational autoencoder (VAE) formalism enables the method to identify and classify rotated and distorted digits. In particular, the conventional objective (cost) function employed…

High resolution galaxy spectra contain much information about galactic physics, but the high dimensionality of these spectra makes it difficult to fully utilize the information they contain. We apply variational autoencoders (VAEs), a…