相关论文: Deep modeling of quasar variability

Common variable star classifiers are built only with the goal of producing the correct class labels, leaving much of the multi-task capability of deep neural networks unexplored. We present a periodic light curve classifier that combines a…

Multi-modal data-sets are ubiquitous in modern applications, and multi-modal Variational Autoencoders are a popular family of models that aim to learn a joint representation of the different modalities. However, existing approaches suffer…

We study the extreme ultraviolet (EUV) variability (rest frame wavelengths 500 - 920 $\AA$) of high luminosity quasars using HST (low to intermediate redshift sample) and SDSS (high redshift sample) archives. The combined HST and SDSS data…

In this paper we review the basic Poissonian formulation of quasar variability, using it as a mathematical tool to extract relevant parameters such as the energy, rate and lifetimes of the flares through the analysis of observed light…

Owing to the advent of large area photometric surveys, the possibility to use broad band photometric data, instead of spectra, to measure the size of the broad line region of active galactic nuclei, has raised a large interest. We describe…

The UV/optical variation, likely driven by accretion disc turbulence, is a defining characteristic of type 1 active galactic nuclei (AGNs) and quasars. In this work we investigate an interesting consequence of such turbulence using quasars…

We analyze the properties of quasar variability using repeated SDSS imaging data in five UV-to-far red photometric bands, accurate to 0.02 mag, for 13,000 spectroscopically confirmed quasars. The observed time lags span the range from 3…

This study aims to improve the spatial representation of uncertainties when regressing surface wind speeds from large-scale atmospheric predictors for sub-seasonal forecasting. Sub-seasonal forecasting often relies on large-scale…

With the growing amount of astronomical data, there is an increasing need for automated data processing pipelines, which can extract scientific information from observation data without human interventions. A critical aspect of these…

We select a sample of 10 radio-quiet quasars with confirmed intranight optical variability and with available X-ray data. We compare the variability properties and the broad band spectral constraints to the predictions of intranight…

Quasars experiencing strong lensing offer unique viewpoints on subjects related to the cosmic expansion rate, the dark matter profile within the foreground deflectors, and the quasar host galaxies. Unfortunately, identifying them in…

The modeling and prediction of multivariate spatio-temporal data involve numerous challenges. Dimension reduction methods can significantly simplify this process, provided that they account for the complex dependencies between variables and…

Analyzing large-scale data from simulations of turbulent flows is memory intensive, requiring significant resources. This major challenge highlights the need for data compression techniques. In this study, we apply a physics-informed Deep…

In just three years, Variational Autoencoders (VAEs) have emerged as one of the most popular approaches to unsupervised learning of complicated distributions. VAEs are appealing because they are built on top of standard function…

Using 16,421 spectra from a sample of 340 quasars ($1.62<z<3.30$) from the SDSS Reverberation Mapping Project, we present an analysis of quasar spectral variability. We confirm the intrinsic Baldwin Effect and brighter-means-bluer trends in…

We present a new approach to analysing the dependence of quasar variability on rest-frame wavelengths. We exploited the spectral archive of the Sloan Digital Sky Survey (SDSS) to create a sample of more than 9000 quasars in the Stripe 82.…

Inverse problems and, in particular, inferring unknown or latent parameters from data are ubiquitous in engineering simulations. A predominant viewpoint in identifying unknown parameters is Bayesian inference where both prior information…

A central challenge in data-driven model discovery is the presence of hidden, or latent, variables that are not directly measured but are dynamically important. Takens' theorem provides conditions for when it is possible to augment these…

Many applications, such as text modelling, high-throughput sequencing, and recommender systems, require analysing sparse, high-dimensional, and overdispersed discrete (count-valued or binary) data. Although probabilistic matrix…

Recent deep learning methods for vessel trajectory prediction are able to learn complex maritime patterns from historical Automatic Identification System (AIS) data and accurately predict sequences of future vessel positions with a…