Related papers: The exact non-Gaussian weak lensing likelihood: A …

Flexion is the significant third-order weak gravitational lensing effect responsible for the weakly skewed and arc-like appearance of lensed galaxies. Here we demonstrate how flexion measurements can be used to measure galaxy halo density…

The magnification bias of large-scale structures, combined with galaxy biasing, leads to a cross-correlation of distant quasars with foreground galaxies on angular scales of the order of arc minutes and larger. The amplitude and angular…

Weak gravitational lensing has become a common tool to constrain the cosmological model. The majority of the methods to derive constraints on cosmological parameters use second-order statistics of the cosmic shear. Despite their success,…

In this article, we investigate posterior convergence in nonparametric regression models where the unknown regression function is modeled by some appropriate stochastic process. In this regard, we consider two setups. The first setup is…

We revise and extend the stochastic approach to cumulative weak lensing (hereafter the sGL method) first introduced in Ref. [1]. Here we include a realistic halo mass function and density profiles to model the distribution of mass between…

We compute the reduced cosmic shear up to second order in the gravitational potential without relying on the small angle or thin-lens approximation. This is obtained by solving the Sachs equation which describes the deformation of the…

We develop a simple analytic model for the gravitational clustering of dark matter haloes to understand how their spatial distribution is biased relative to that of the mass. The statistical distribution of dark haloes within the initial…

Non-Gaussianity in the cosmic microwave background and the large-scale structure of galaxies provides an increasingly powerful probe of the universe. I implement an algorithm to generate realisations of fields that possess an arbitrary…

In many cosmological inference problems, the likelihood (the probability of the observed data as a function of the unknown parameters) is unknown or intractable. This necessitates approximations and assumptions, which can lead to incorrect…

In this paper, we show that Minkowski Functionals (MFs) of weak gravitational lensing (WL) convergence maps contain significant non-Gaussian, cosmology-dependent information. To do this, we use a large suite of cosmological ray-tracing…

Using data from the HST Medium Deep Survey, a long-term Key Project, together with generically similar archived data, we have discovered evidence for weak gravitational `shear' of background field galaxies (I = 22 -- 26) in the vicinity of…

We report the first measurement of genus curves for the two-dimensional mass map in the neighborhood of rich, X-ray luminous galaxy cluster MS1054-03 at z=0.83, reconstructed from weak lensing data obtained by Suprime-Cam on the prime focus…

We use high-resolution dissipationless simulations of the concordance flat LCDM model to make predictions for the galaxy--mass (GM) correlations and compare them to the recent SDSS weak lensing measurements.We use a simple observationally…

For Gaussian initial conditions the perturbation theory predicts a very specific hierarchy for the projected matter $p$-point correlation functions. In the small angle approximation and assuming a power-law spectrum I derive the exact…

Estimators for weak lensing observables such as shear and convergence generally have non-linear corrections, which, in principle, make weak lensing power spectra sensitive to primordial non-Gaussianity. In this paper, we quantitatively…

Large scale correlations in the orientations of galaxies can result from alignments in their angular momentum vectors. These alignments arise from the tidal torques exerted on neighboring proto-galaxies by the smoothly varying shear field.…

Likelihood fitting to two-point clustering statistics made from galaxy surveys usually assumes a multivariate normal distribution for the measurements, with justification based on the central limit theorem given the large number of…

Gaussian process regression is a powerful Bayesian nonlinear regression method. Recent research has enabled the capture of many types of observations using non-Gaussian likelihoods. To deal with various tasks in spatial modeling, we benefit…

We investigate the level of accuracy and precision of cluster weak-lensing (WL) masses measured with the \Euclid data processing pipeline. We use the DEMNUni-Cov $N$-body simulations to assess how well the WL mass probes the true halo mass,…

Many approximate Bayesian inference methods assume a particular parametric form for approximating the posterior distribution. A multivariate Gaussian distribution provides a convenient density for such approaches; examples include the…