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Variational Autoencoders (VAEs) are powerful generative models that have been widely used in various fields, including image and text generation. However, one of the known challenges in using VAEs is the model's sensitivity to its…
Compact radio AGN are thought to be young radio active galactic nuclei (AGN) at the early stage of AGN evolution, thus are ideal laboratory to study the high-energy emission throughout the evolution of radio AGN. In this work, we report for…
Variational autoencoders (VAEs) are a powerful class of deep generative latent variable model for unsupervised representation learning on high-dimensional data. To ensure computational tractability, VAEs are often implemented with a…
The Variational Autoencoder (VAE) is known to suffer from the phenomenon of \textit{posterior collapse}, where the latent representations generated by the model become independent of the inputs. This leads to degenerated representations of…
An empirical forward-modeling framework is developed to interpret the multiwavelength properties of Active Galactic Nuclei (AGN) and provide insights into the overlap and incompleteness of samples selected at different parts of the…
The variational autoencoder (VAE) is a simple and efficient generative artificial intelligence method for modeling complex probability distributions of various types of data, such as images and texts. However, it suffers some main…
We present the drastic transformation of the X-ray properties of the active galactic nucleus 1ES 1927+654, following a changing-look event. After the optical/UV outburst the power-law component, produced in the X-ray corona, disappeared,…
We present a systematic X-ray and multiwavelength study of a sample of 47 active galactic nuclei (AGNs) with reverberation-mapping measurements. This sample includes 21 super-Eddington accreting AGNs and 26 sub-Eddington accreting AGNs.…
We propose an algorithm, guided variational autoencoder (Guided-VAE), that is able to learn a controllable generative model by performing latent representation disentanglement learning. The learning objective is achieved by providing…
The observed relation between the X-ray radiation from AGNs, originating in the corona, and the optical/UV radiation from the disk is usually described by the anticorrelation between the UV to X-ray slope alpha_ox and the UV luminosity.…
Active galactic nuclei (AGN) are extremely variable in the X-ray band down to very short timescales. However, the driver behind the X-ray variability is still poorly understood. Previous results suggest that the hot corona responsible for…
Variable X-ray absorption has been observed in active galactic nuclei (AGN) on several time scales. Observations allow us to identify the absorber with clouds associated either with the clumpy torus (parsec scales, long timescales) or with…
Given a dataset of images containing different objects with different features such as shape, size, rotation, and x-y position; and a Variational Autoencoder (VAE); creating a disentangled encoding of these features in the hidden space…
We present a coupled Variational Auto-Encoder (VAE) method that improves the accuracy and robustness of the probabilistic inferences on represented data. The new method models the dependency between input feature vectors (images) and weighs…
Advancements in optical quantum technologies have been enabled by the generation, manipulation, and characterization of light, with identification based on its photon statistics. However, characterizing light and its sources through single…
Variational autoencoders (VAEs) are used for transfer learning across various research domains such as music generation or medical image analysis. However, there is no principled way to assess before transfer which components to retrain or…
Recently, nearby active galactic nuclei (AGN) have been subject to long X-rays/UV/optical monitoring campaigns. These campaigns reveal a strong correlation between the various UV and optical bands, with time lags increasing with wavelength.…
Time-domain studies of active galactic nuclei (AGNs) offer a powerful tool for understanding black hole accretion physics. Prior to the optical outburst on 23 December 2017, 1ES 1927+654 was classified as a "true" type~2 AGN, an unobscured…
Paradoxically, a Variational Autoencoder (VAE) could be pushed in two opposite directions, utilizing powerful decoder model for generating realistic images but collapsing the learned representation, or increasing regularization coefficient…
The UV/optical variability of AGN has long been thought to be driven by the X-ray illumination of the accretion disk. However, recent multi-wavelength campaigns of nearby Seyfert galaxies seem to challenge this paradigm, with an apparent…