Related papers: Deep modeling of quasar variability
We present the results of a search for variability in the equivalent widths (EWs) of narrow, associated (Delta v < 5,000 km/s) absorption lines found in the UV spectra of z < 1.5 quasars. The goal of this search was to use variability as a…
Quasars,asextremelyluminousanddistantspecialcelestialbodiesintheuniverse,aredrivenbyacomplexsystemcomposedof supermassiveblackholesandsurroundingaccretiondisks.Thispaperadoptsatime-domainobservationstrategyandcombines the analysis of light…
We quantify quasar color-variability using an unprecedented variability database - ugriz photometry of 9093 quasars from SDSS Stripe 82, observed over 8 years at ~60 epochs each. We confirm previous reports that quasars become bluer when…
We develop a method for separating quasars from other variable point sources using SDSS Stripe 82 light curve data for ~10,000 variable objects. To statistically describe quasar variability, we use a damped random walk model parametrized by…
Macromolecular and biomolecular folding landscapes typically contain high free energy barriers that impede efficient sampling of configurational space by standard molecular dynamics simulation. Biased sampling can artificially drive the…
Incorporating nonlinearity is paramount to predicting the future states of a dynamical system, its response to shocks, and its underlying causal network. However, most existing methods for causality detection and impulse response, such as…
In recent years, the field of machine learning has made phenomenal progress in the pursuit of simulating real-world data generation processes. One notable example of such success is the variational autoencoder (VAE). In this work, with a…
The framework of variational autoencoders allows us to efficiently learn deep latent-variable models, such that the model's marginal distribution over observed variables fits the data. Often, we're interested in going a step further, and…
The relation between X-ray and UV/optical variability in AGNs has been explored in many individual sources, however a large sample study is yet absent. Through matching the XMM-Newton serendipitous X-ray and UV source catalogs with SDSS…
The well-known bluer-when-brighter trend observed in quasar variability is a signature of the complex processes in the accretion disk, and can be a probe of the quasar variability mechanism. Using a sample of 604 variable quasars with…
Variational autoencoders often assume isotropic Gaussian priors and mean-field posteriors, hence do not exploit structure in scenarios where we may expect similarity or consistency across latent variables. Gaussian process variational…
A damped random walk (DRW) process is often used to describe the temporal UV/optical continuum variability of active galactic nuclei (AGN). However, recent investigations have shown that this model fails to capture the full spectrum of AGN…
UV/optical variability in quasars is a well-observed phenomenon, yet its primeval origins remain unclear. This study investigates whether the accretion disk turbulence, which is responsible for UV/optical variability, is influenced by the…
We model the time variability of ~9,000 spectroscopically confirmed quasars in SDSS Stripe 82 as a damped random walk. Using 2.7 million photometric measurements collected over 10 years, we confirm the results of Kelly et al. (2009) and…
Among other uses, neural networks are a powerful tool for solving deterministic and Bayesian inverse problems in real-time, where variational autoencoders, a specialized type of neural network, enable the Bayesian estimation of model…
Predicting high-dimensional dynamical systems with irregular time steps presents significant challenges for current data-driven algorithms. These irregularities arise from missing data, sparse observations, or adaptive computational…
The main purpose of this work is to improve the existing knowledge about the most powerful engines in the Universe - quasars. Although a lot is already known, we still have only a vague idea how these engines work exactly, why they behave…
The study of quasar variability has long been seen as a way to understanding the structure of the central engine of active galactic nuclei, and as a means of verifying the morphology of the standard model. Much work has already been done on…
Deep neural networks with discrete latent variables offer the promise of better symbolic reasoning, and learning abstractions that are more useful to new tasks. There has been a surge in interest in discrete latent variable models, however,…
Existing black box modeling approaches in machine learning suffer from a fixed input and output feature combination. In this paper, a new approach to reconstruct missing variables in a set of time series is presented. An autoencoder is…