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20212026
most citedEuclid. III. The NISP Instrument

114 citations · 551 across the 126 of their papers we have counts for

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Showing 2024 · astro-ph.COShow all

15 papers · 2 filters

astro-ph.CO2024

Euclid preparation: TBD. The impact of line-of-sight projections on the covariance between galaxy cluster multi-wavelength observable properties -- insights from hydrodynamic simulations

Euclid Collaboration, A. Ragagnin, A. Saro +240

Cluster cosmology can benefit from combining multi-wavelength studies, which can benefit from characterising the correlation coefficients between different mass-observable relation…

astro-ph.CO2024★ 4 cited

Euclid preparation. The impact of relativistic redshift-space distortions on two-point clustering statistics from the Euclid wide spectroscopic survey

Euclid Collaboration, M. Y. Elkhashab, D. Bertacca +252

Measurements of galaxy clustering are affected by RSD. Peculiar velocities, gravitational lensing, and other light-cone projection effects modify the observed redshifts, fluxes, an…

astro-ph.CO2024

Euclid preparation: 6x2 pt analysis of Euclid's spectroscopic and photometric data sets

Euclid Collaboration, L. Paganin, M. Bonici +252

We present cosmological parameter forecasts for the Euclid 6x2pt statistics, which include the galaxy clustering and weak lensing main probes together with previously neglected cro…

astro-ph.CO2024★ 6 cited

Euclid preparation. Simulations and nonlinearities beyond CDM. 1. Numerical methods and validation

Euclid Collaboration, J. Adamek, B. Fiorini +268

To constrain models beyond CDM, the development of the Euclid analysis pipeline requires simulations that capture the nonlinear phenomenology of such models. We present an overv…

astro-ph.CO2024★ 1 cited

Euclid preparation. L. Calibration of the linear halo bias in CDM cosmologies

Euclid Collaboration, T. Castro, A. Fumagalli +253

The Euclid mission, designed to map the geometry of the dark Universe, presents an unprecedented opportunity for advancing our understanding of the cosmos through its photometric g…

astro-ph.CO2024★ 3 cited

Euclid preparation. LXVII. Deep learning true galaxy morphologies for weak lensing shear bias calibration

Euclid Collaboration, B. Csizi, T. Schrabback +259

To date, galaxy image simulations for weak lensing surveys usually approximate the light profiles of all galaxies as a single or double Sérsic profile, neglecting the influence of…