Related papers: Trans-Dimensional Bayesian Inference for Gravitati…
We have previously reported the discovery of strong gravitational lensing by faint elliptical galaxies using the WFPC2 on HST and here we investigate their potential usefulness in putting constraints on lens mass models. We compare various…
It is now practically the norm for data to be very high dimensional in areas such as genetics, machine vision, image analysis and many others. When analyzing such data, parametric models are often too inflexible while nonparametric…
The dark and dynamic parts of the Galaxy, including the bulk shape and movement of the Galactic Bulge and characteristics of dark compact object populations, such as a hypothetical population of primordial black holes (PBHs), are difficult…
Gravitational lensing is the deflection of light rays due to the gravity of intervening masses. This phenomenon is observed in a variety of scales and configurations, involving any non-uniform mass such as planets, stars, galaxies, clusters…
Estimation of spatially-varying parameters for computationally expensive forward models governed by partial differential equations is addressed. A novel multiscale Bayesian inference approach is introduced based on deep probabilistic…
We discuss whether one should expect that multiply imaged QSOs can be understood with `simple' lens models which contain a handful of parameters. Whereas for many lens systems such simple mass models yield a remarkably good description of…
Galaxy-scale strong lenses in galaxy clusters provide a unique tool to investigate their inner mass distribution and the sub-halo density profiles in the low-mass regime, which can be compared with the predictions from cosmological…
[ABRIDGED] The weak gravitational lensing effect is used to infer matter density fluctuations within the field-of-view of the Garching-Bonn Deep Survey (GaBoDS). This information is employed for a statistical comparison of the galaxy…
A novel approach of accurately reconstructing storage ring's linear optics from turn-by-turn (TbT) data containing measurement error is introduced. This approach adopts a Bayesian inference based on the Markov Chain Monte-Carlo (MCMC)…
We present a Bayesian graph neural network (BGNN) that can estimate the weak lensing convergence ($\kappa$) from photometric measurements of galaxies along a given line of sight. The method is of particular interest in strong gravitational…
Gravitational lensing has become one of the most interesting tools to study the mass distribution in the Universe. Since gravitational light deflection is independent of the nature and state of the matter, it is ideally suited to…
We apply Markov Chain Monte Carlo (MCMC) to the problem of parametric galaxy modeling, estimating posterior distributions of galaxy properties such as ellipticity and brightness for more than 100,000 images of galaxies taken from DC2, a…
We have determined the mass-density radial profiles of the first five strong gravitational lens systems discovered by the Herschel Astrophysical Terahertz Large Area Survey (H-ATLAS). We present an enhancement of the semi-linear lens…
Most modern imaging systems incorporate a computational pipeline to infer the image of interest from acquired measurements. The Bayesian approach to solve such ill-posed inverse problems involves the characterization of the posterior…
We introduce a new method for constraining the redshift distribution of a set of galaxies, using weak gravitational lensing shear. Instead of using observed shears and redshifts to constrain cosmological parameters, we ask how well the…
Despite the success of galaxy-scale strong gravitational lens studies with Hubble-quality imaging, the number of well-studied strong lenses remains small. As a result, robust comparisons of the lens models to theoretical predictions are…
Strong gravitational lensing is a unique observational tool for studying the dark and luminous mass distribution both within and between galaxies. Given the presence of substructures, current strong lensing observations demand more complex…
It is widely recognized that cold dark matter models predict abundant dark matter substructure in halos of all sizes. Galaxy-galaxy lensing provides a unique opportunity to directly measure the presence and the mass of such substructures in…
We present the analysis of a sample of twenty-four SLACS-like galaxy-galaxy strong gravitational lens systems with a background source and deflectors from the Illustris-1 simulation. We study the degeneracy between the complex mass…
The Bayesian elastic net regression model is characterized by the regression coefficient prior distribution, the negative log density of which corresponds to the elastic net penalty function. While Markov chain Monte Carlo (MCMC) methods…