114 citations · 1.7k across the 162 of their papers we have counts for
9 papers · 1 filter
Euclid: Covariance of weak lensing pseudo- estimates. Calculation, comparison to simulations, and dependence on survey geometry
R. E. Upham, M. L. Brown, L. Whittaker +102
An accurate covariance matrix is essential for obtaining reliable cosmological results when using a Gaussian likelihood. In this paper we study the covariance of pseudo- es…
Euclid: Forecast constraints on consistency tests of the CDM model
S. Nesseris, D. Sapone, M. Martinelli +112
The standard cosmological model is based on the fundamental assumptions of a spatially homogeneous and isotropic universe on large scales. An observational detection of a violation…
Euclid preparation: XIX. Impact of magnification on photometric galaxy clustering
F. Lepori, I. Tutusaus, C. Viglione +183
We investigate the importance of lensing magnification for estimates of galaxy clustering and its cross-correlation with shear for the photometric sample of Euclid. Using updated s…
Euclid preparation: I. The Euclid Wide Survey
R. Scaramella, J. Amiaux, Y. Mellier +238
Euclid is an ESA mission designed to constrain the properties of dark energy and gravity via weak gravitational lensing and galaxy clustering. It will carry out a wide area imaging…
Euclid: Forecasts from redshift-space distortions and the Alcock-Paczynski test with cosmic voids
N. Hamaus, M. Aubert, A. Pisani +127
Euclid is poised to survey galaxies across a cosmological volume of unprecedented size, providing observations of more than a billion objects distributed over a third of the full s…
Dark Energy Survey Year 3 Results: Galaxy mock catalogs for BAO analysis
I. Ferrero, M. Crocce, I. Tutusaus +83
The calibration and validation of scientific analysis in simulations is a fundamental tool to ensure unbiased and robust results in observational cosmology. In particular, mock gal…
preparation: XV. Forecasting cosmological constraints for the and CMB joint analysis
Euclid Collaboration, S. Ilić, N. Aghanim +207
The combination and cross-correlation of the upcoming data with cosmic microwave background (CMB) measurements is a source of great expectation since it will provide the l…
Euclid preparation: XIII. Forecasts for galaxy morphology with the Euclid Survey using Deep Generative Models
Euclid Collaboration, H. Bretonnière, M. Huertas-Company +193
We present a machine learning framework to simulate realistic galaxies for the Euclid Survey. The proposed method combines a control on galaxy shape parameters offered by analytic…
The PAU survey: Estimating galaxy photometry with deep learning
Laura Cabayol, Martin Eriksen, Adam Amara +14
With the dramatic rise in high-quality galaxy data expected from Euclid and Vera C. Rubin Observatory, there will be increasing demand for fast high-precision methods for measuring…