Related papers: Accelerating lensed quasar discovery and modeling …

We develop data-driven methods for incorporating physical information for priors to learn parsimonious representations of nonlinear systems arising from parameterized PDEs and mechanics. Our approach is based on Variational Autoencoders…

The microlensing signal in the light curves of gravitationally lensed quasars can shed light on the dark matter (DM) composition in their lensing galaxies. Here, we investigate a sample of six lensed quasars from the most recent and best…

We present a parametric strong-lensing model for the galaxy cluster MACS J1931.8-2635 ($z_l = 0.35$), accompanying the detection of the spectroscopically confirmed SN Eos at $z = 5.13$ (Coulter et al. 2026). We identify 10 new…

Variational Autoencoders (VAEs) are well-established as a principled approach to probabilistic unsupervised learning with neural networks. Typically, an encoder network defines the parameters of a Gaussian distributed latent space from…

Variations in scaling behavior in the flux and emissions of gravitational lensed quasars can provide valuable information about the dynamics within the sources and their cosmological evolution with time. Here, we study the multifractal…

A variational autoencoder (VAE) is a probabilistic machine learning framework for posterior inference that projects an input set of high-dimensional data to a lower-dimensional, latent space. The latent space learned with a VAE offers…

We present a new method of discovering galaxy-scale, strongly-lensed QSO systems from unresolved light curves using the autocorrelation function. The method is tested on five rungs of simulated light curves from the Time Delay Challenge 1…

The field of Astronomy requires the collection and assimilation of vast volumes of data. The data handling and processing problem has become severe as the sheer volume of data produced by scientific instruments each night grows…

Learning from heterogeneous data poses challenges such as combining data from various sources and of different types. Meanwhile, heterogeneous data are often associated with missingness in real-world applications due to heterogeneity and…

Convolutional Neural Networks (ConvNets) are one of the most promising methods for identifying strong gravitational lens candidates in survey data. We present two ConvNet lens-finders which we have trained with a dataset composed of real…

We present variability and multi-wavelength photometric information for the 933 known quasars in the QUEST Variability Survey. These quasars are grouped into variable and non-variable populations based on measured variability confidence…

Gravitationally-lensed quasars can be discovered as a by-product of galaxy redshift surveys. Lenses discovered spectroscopically in this way should require less observational effort per event than those found in dedicated lens surveys.…

We report the discovery of five new doubly-imaged lensed quasars from the first 2500 square degrees of the ongoing Canada-France Imaging Survey (CFIS), which is a component of the Ultraviolet Near Infrared Optical Northern Survey (UNIONS),…

This work studies the problem of modeling visual processes by leveraging deep generative architectures for learning linear, Gaussian representations from observed sequences. We propose a joint learning framework, combining a vector…

Recent advances in deep learning have shown their ability to learn strong feature representations for images. The task of image clustering naturally requires good feature representations to capture the distribution of the data and…

Microlensing by stars within distant galaxies acting as strong gravitational lenses of multiply-imaged quasars, provides a unique and direct measurement of the internal structure of the lensed quasar on nano-arcsecond scales. The…

Gravitational lensing can amplify the apparent brightness of distant sources. Images that are highly magnified are often part of multiply-imaged systems, but we consider the possibility of having large magnifications without additional…

The distribution of dark and luminous matter can be mapped around galaxies that gravitationally lens background objects into arcs or Einstein rings. New surveys will soon observe hundreds of thousands of galaxy lenses, and current,…

We introduce a deep machine learning approach to studying quasar microlensing light curves for the first time by analyzing hundreds of thousands of simulated light curves with respect to the accretion disc size and temperature profile. Our…

We present new lensing frequency estimates for existing and forthcoming deep near-infrared surveys, including those from JWST and VISTA. The estimates are based on the JAdes extraGalactic Ultradeep Artificial Realisations (JAGUAR) galaxy…