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Weak gravitational lensing is a very sensitive way of measuring cosmological parameters, including dark energy, and of testing current theories of gravitation. In practice, this requires exquisite measurement of the shapes of billions of galaxies over large areas of the sky, as may be obtained with the EUCLID and WFIRST satellites. For a given survey depth, applying image denoising to the data both improves the accuracy of the shape measurements and increases the number density of galaxies with a measurable shape. We perform simple tests of three different denoising techniques, using synthetic data. We propose a new and simple denoising method, based on wavelet decomposition of the data and a Wiener filtering of the resulting wavelet coefficients. When applied to the GREAT08 challenge dataset, this technique allows us to improve the quality factor of the measurement (Q; GREAT08 definition), by up to a factor of two. We demonstrate that the typical pixel size of the EUCLID optical channel will allow us to use image denoising.
One of the most powerful techniques to study the dark sector of the Universe is weak gravitational lensing. In practice, to infer the reduced shear, weak lensing measures galaxy shapes, which are the consequence of both the intrinsic ellipticity of t
Accurate estimation of the merger timescale of galaxy clusters is important to understand the cluster merger process and further the formation and evolution of the large-scale structure of the universe. In this paper, we explore a baryonic effect on
Cold fronts -- contact discontinuities in the intracluster medium (ICM) of galaxy clusters -- should be disrupted by Kelvin-Helmholtz (K-H) instabilities due to the associated shear velocity. However, many observed cold fronts appear stable. This ope
Using a set of high-resolution simulations we study the statistical correlation of dark matter halo properties with the large-scale environment. We consider halo populations split into four Cosmic Web (CW) elements: voids, walls, filaments, and nodes
We present a determination of the effects of including galaxy morphological parameters in photometric redshift estimation with an artificial neural network method. Neural networks, which recognize patterns in the information content of data in an unb