497 citations · 1.1k across the 12 of their papers we have counts for
12 papers
Interpretability of deep-learning methods applied to large-scale structure surveys
Gaspard Aymerich, Tomasz Kacprzak, Alexandre Refregier
Deep learning and convolutional neural networks in particular are powerful and promising tools for cosmological analysis of large-scale structure surveys. They are already providin…
Fast Generation of Covariance Matrices for Weak Lensing
Raphael Sgier, Alexandre Réfrégier, Adam Amara +1
Upcoming weak lensing surveys will probe large fractions of the sky with unprecedented accuracy. To infer cosmological constraints, a large ensemble of survey simulations are requi…
The Dark Energy Survey Data Release 1
T. M. C. Abbott, F. B. Abdalla, S. Allam +199
We describe the first public data release of the Dark Energy Survey, DES DR1, consisting of reduced single epoch images, coadded images, coadded source catalogs, and associated pro…
Lensing substructure quantification in RXJ1131-1231: A 2 keV lower bound on dark matter thermal relic mass
Simon Birrer, Adam Amara, Alexandre Refregier
We study the substructure content of the strong gravitational lens RXJ1131-1231 through a forward modelling approach that relies on generating an extensive suite of realistic simul…
Line-of-sight effects in strong lensing: Putting theory into practice
Simon Birrer, Cyril Welschen, Adam Amara +1
We present a simple method to accurately infer line of sight (LOS) integrated lensing effects for galaxy scale strong lens systems through image reconstruction. Our approach enable…
Inference from the small scales of cosmic shear with current and future Dark Energy Survey data
N. MacCrann, J. Aleksić, A. Amara +61
Cosmic shear is sensitive to fluctuations in the cosmological matter density field, including on small physical scales, where matter clustering is affected by baryonic physics in g…