550 citations · 2.2k across the 241 of their papers we have counts for
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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 preparation: XVIII. Cosmic Dawn Survey. Spitzer observations of the Euclid deep fields and calibration fields
Andrea Moneti, H. J. McCracken, M. Shuntov +194
We present a new infrared survey covering the three Euclid deep fields and four other Euclid calibration fields using Spitzer's Infrared Array Camera (IRAC). We have combined these…
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: Constraining ensemble photometric redshift distributions with stacked spectroscopy
M. S. Cagliari, B. R. Granett, L. Guzzo +109
The ESA Euclid mission will produce photometric galaxy samples over 15000 square degrees of the sky that will be rich for clustering and weak lensing statistics. The accuracy of th…
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…
Euclid: constraining dark energy coupled to electromagnetism using astrophysical and laboratory data
M. Martinelli, C. J. A. P. Martins, S. Nesseris +92
In physically realistic scalar-field based dynamical dark energy models (including, e.g., quintessence) one naturally expects the scalar field to couple to the rest of the model's…
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…