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most citedPlanck 2013 results. XVI. Cosmological parameters

6.6k citations · 37.2k across the 272 of their papers we have counts for

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astro-ph.GA2021★ 80 cited

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…

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.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★ 2 cited

Euclid Preparation: XIV. The Complete Calibration of the Color-Redshift Relation (C3R2) Survey: Data Release 3

Euclid Collaboration, S. A. Stanford, D. Masters +183

The Complete Calibration of the Color-Redshift Relation (C3R2) survey is obtaining spectroscopic redshifts in order to map the relation between galaxy color and redshift to a depth…

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…