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20072026
most citedEuclid preparation: IX. EuclidEmulator2 -- Power spectrum emulation with massive neutrinos and self-consistent dark energy perturbations

166 citations · 1.5k across the 124 of their papers we have counts for

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Showing 2021Show all

7 papers · 1 filter

astro-ph.CO2021★ 56 cited

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…

astro-ph.IM2021★ 43 cited

Euclid preparation: XVI. Exploring the ultra low-surface brightness Universe with Euclid/VIS

A. S. Borlaff, P. Gómez-Alvarez, B. Altieri +180

While Euclid is an ESA mission specifically designed to investigate the nature of Dark Energy and Dark Matter, the planned unprecedented combination of survey area ( d…

astro-ph.CO2021★ 8 cited

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…

astro-ph.CO2021★ 68 cited

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…

astro-ph.CO2021★ 78 cited

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…

astro-ph.GA2021★ 32 cited

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…

astro-ph.CO2021★ 26 cited

Euclid preparation: XII. Optimizing the photometric sample of the Euclid survey for galaxy clustering and galaxy-galaxy lensing analyses

Euclid Collaboration, A. Pocino, I. Tutusaus +157

The accuracy of photometric redshifts (photo-zs) particularly affects the results of the analyses of galaxy clustering with photometrically-selected galaxies (GCph) and weak lensin…