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19952022
most citedThe Shear TEsting Programme 2: Factors affecting high precision weak lensing analyses

402 citations · 1.9k across the 23 of their papers we have counts for

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Showing 2019Show all

9 papers · 1 filter

astro-ph.CO2019

Baryonic effects for weak lensing. Part II. Combination with X-ray data and extended cosmologies

Aurel Schneider, Alexandre Refregier, Sebastian Grandis +6

An accurate modelling of baryonic feedback effects is required to exploit the full potential of future weak-lensing surveys such as Euclid or LSST. In this second paper in a series…

astro-ph.CO2019

Baryonic effects for weak lensing. Part I. Power spectrum and covariance matrix

Aurel Schneider, Nicola Stoira, Alexandre Refregier +4

Baryonic feedback effects lead to a suppression of the weak lensing angular power spectrum on small scales. The poorly constrained shape and amplitude of this suppression is an imp…

astro-ph.CO2019

Cross-correlating 21 cm and galaxy surveys: implications for cosmology and astrophysics

Hamsa Padmanabhan, Alexandre Refregier, Adam Amara

We forecast astrophysical and cosmological parameter constraints from synergies between 21 cm intensity mapping and wide field optical galaxy surveys (both spectroscopic and photom…

physics.comp-ph2019

Cosmological N-body simulations: a challenge for scalable generative models

Nathanaël Perraudin, Ankit Srivastava, Aurelien Lucchi +3

Deep generative models, such as Generative Adversarial Networks (GANs) or Variational Autoencoders (VAs) have been demonstrated to produce images of high visual quality. However, t…

astro-ph.CO2019

Monte Carlo Control Loops for cosmic shear cosmology with DES Year 1

T. Kacprzak, J. Herbel, A. Nicola +52

Weak lensing by large-scale structure is a powerful probe of cosmology and of the dark universe. This cosmic shear technique relies on the accurate measurement of the shapes and re…

astro-ph.CO2019

Cosmological constraints with deep learning from KiDS-450 weak lensing maps

Janis Fluri, Tomasz Kacprzak, Aurelien Lucchi +4

Convolutional Neural Networks (CNN) have recently been demonstrated on synthetic data to improve upon the precision of cosmological inference. In particular they have the potential…