Related papers: Extracting the X-ray reverberation response functi…
Given the notably increasing complexity of mathematical models to study realistic systems and their coupling to their environment that constrains their dynamics, both analytical approaches and numerical methods that build on these models,…
Variational auto-encoders (VAEs) provide an attractive solution to image generation problem. However, they tend to produce blurred and over-smoothed images due to their dependence on pixel-wise reconstruction loss. This paper introduces a…
We show that X-ray reverberation mapping can be used to measure the distance to type 1 active galactic nuclei (AGNs). This is because X-ray photons originally emitted from the `corona' close to the black hole irradiate the accretion disc…
The electrocardiogram (ECG) is an inexpensive and widely available tool for cardiac assessment. Despite its standardized format and small file size, the high complexity and inter-individual variability of ECG signals (typically a…
Swift intensive accretion disk reverberation mapping of four AGN yielded light curves sampled $\sim$200-350 times in 0.3-10 keV X-ray and six UV/optical bands. Uniform reduction and cross-correlation analysis of these datasets yields three…
The illumination pattern (or emissivity profile) of the accretion disc due to the reflection of X-rays in AGN can be understood in terms of relativistic effects on the rays propagating from a source in a corona surrounding the central black…
We develop a new spectral model for the broadband spectral energy distribution (SED) of Active Galactic Nuclei (AGN). This includes an outer standard disc, an inner warm Comptonising region to produce the soft X-ray excess and a hot corona.…
The X-ray spectra of many active galactic nuclei (AGN) show a soft X-ray excess below 1-2 keV on top of the extrapolated high- energy power law. The origin of this component is uncertain. It could be a signature of relativistically blurred,…
It is established that the ultraviolet (UV) and X-ray emissions in active galactic nuclei (AGN) are tightly correlated. This correlation is observed both in low- and high-redshift sources. In particular, observations of large samples of…
We present the results from an X-ray variability study of IRAS 13224-3809. This is probably the best source for X-ray reverberation studies since it is X-ray bright, extremely variable, and it has been extensively observed with XMM-Newton.…
We introduce a deep learning method to simulate the motion of particles trapped in a chaotic recirculating flame. The Lagrangian trajectories of particles, captured using a high-speed camera and subsequently reconstructed in 3-dimensional…
Continuum reverberation mapping with high-cadence, long-term UV/optical monitoring of Active Galactic Nuclei (AGNs) enables us to resolve the AGN central engine sizes on different timescales. The frequency-resolved time lags of NGC 5548…
In end-to-end optimized learned image compression, it is standard practice to use a convolutional variational autoencoder with generalized divisive normalization (GDN) to transform images into a latent space. Recently, Operational Neural…
Relativistically blurred reflection from the accretion disc provides a powerful probe of the extreme environments close to supermassive black holes; the inner regions of the accretion flow and the corona that produces the intense X-ray…
Variational autoencoder (VAE) is one of the most common techniques in the field of medical image generation, where this architecture has shown advanced researchers in recent years and has developed into various architectures. VAE has…
We fit a new vertically extended corona model to previously measured reverberation time lags observed by \emph{XMM-Newton} in two extremely variable Narrow Line Seyfert 1 Active Galactic Nuclei (AGN), 1H~0707-495 and IRAS~13224-3809, in a…
Does a Variational AutoEncoder (VAE) consistently encode typical samples generated from its decoder? This paper shows that the perhaps surprising answer to this question is `No'; a (nominally trained) VAE does not necessarily amortize…
Representation learning seeks to expose certain aspects of observed data in a learned representation that's amenable to downstream tasks like classification. For instance, a good representation for 2D images might be one that describes only…
Learning from an imbalanced distribution presents a major challenge in predictive modeling, as it generally leads to a reduction in the performance of standard algorithms. Various approaches exist to address this issue, but many of them…
We propose a new method for testing antenna arrays that records the radiating electromagnetic (EM) field using an absorbing material and evaluating the resulting thermal image series through an AI using a conditional encoder-decoder model.…