Related papers: Accelerating lensed quasar discovery and modeling …
Context. Large, high-dimensional astronomical surveys require efficient data analysis. Automatic fitting of lightcurve variability and machine learning may assist in identification of sources including candidate quasars. Aims. We aim to…
We anticipate that hundreds of thousands of distant, strongly gravitationally lensed sources will be detectable with the European Space Agency's (ESA) Euclid mission and the Rubin Observatory Legacy Survey of Space and Time. We consider the…
Variational Autoencoders (VAEs) are powerful generative models for learning latent representations. Standard VAEs generate dispersed and unstructured latent spaces by utilizing all dimensions, which limits their interpretability, especially…
We present the second public data release (DR) of the VISTA EXtension to Auxiliary Surveys (VEXAS), where we classify objects into stars, galaxies and quasars based on an ensemble of machine learning algorithms. The aim of VEXAS is to build…
Quasars acting as strong gravitational lenses offer a rare opportunity to probe the redshift evolution of scaling relations between supermassive black holes and their host galaxies, particularly the $M_{\mathrm{BH}}$--$M_{\mathrm{host}}$…
SDSS J2222+2745, at z = 0.489, is one of the few currently known lens clusters with multiple images of a background (z = 2.801) quasar with measured time delays. We combine imaging from the Hubble Space Telescope (HST) with recent Multi…
Accurate redshift determinations of both lenses and sources are critical for confirming strong-lens systems and fully realizing their scientific value. However, the thousands of strong-lens candidates now routinely discovered in wide-field…
The Hubble tension, revealed by a $\sim 5\sigma$ discrepancy between measurements of the Hubble-Lemaitre constant from early- and local-Universe observations, is one of the most significant problems in modern cosmology. In order to better…
Strong gravitational lensing systems with multiple source planes are powerful tools for probing the density profiles and dark matter substructure of the galaxies. The ratio of Einstein radii is related to the dark energy equation of state…
The framework of variational autoencoders (VAEs) provides a principled method for jointly learning latent-variable models and corresponding inference models. However, the main drawback of this approach is the blurriness of the generated…
Strong gravitational lensing distorts our view of sources at cosmological distances but brings invaluable constraints on the mass content of foreground objects and on the geometry and properties of the Universe. We report the detection of a…
To date, galaxy image simulations for weak lensing surveys usually approximate the light profiles of all galaxies as a single or double S\'ersic profile, neglecting the influence of galaxy substructures and morphologies deviating from such…
Weak gravitational lensing provides a potentially powerful method for the detection of clusters. In addition to cluster candidates, a large number of objects with possibly no optical or X-ray component have been detected in shear-selected…
Quantifying image distortions caused by strong gravitational lensing and estimating the corresponding matter distribution in lensing galaxies has been primarily performed by maximum likelihood modeling of observations. This is typically a…
Of the hundreds of $z\gtrsim6$ quasars discovered to date, only one is known to be gravitationally lensed, despite the high lensing optical depth expected at $z\gtrsim6$. High-redshift quasars are typically identified in large-scale surveys…
Quadruply lensed quasars are extremely rare objects, but incredibly powerful cosmological tools. Only few dozen are known in the whole sky. Here we present the spectroscopic confirmation of two new quadruplets WG0214-2105 and WG2100-4452…
Cosmic voids are an important probe of large-scale structure that can constrain cosmological parameters and test cosmological models. We present a new paradigm for void studies: void detection in weak lensing convergence maps. This approach…
Variational autoencoders are powerful algorithms for identifying dominant latent structure in a single dataset. In many applications, however, we are interested in modeling latent structure and variation that are enriched in a target…
Variational autoencoders (VAEs) have been used extensively to discover low-dimensional latent factors governing neural activity and animal behavior. However, without careful model selection, the uncovered latent factors may reflect noise in…
Quantifying the parameters and corresponding uncertainties of hundreds of strongly lensed quasar systems holds the key to resolving one of the most important scientific questions: the Hubble constant ($H_{0}$) tension. The commonly used…