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20102026
most citedThe Cosmological Analysis of the SDSS/BOSS data from the Effective Field Theory of Large-Scale Structure

456 citations · 2.8k across the 141 of their papers we have counts for

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Showing 2022Show all

10 papers · 1 filter

astro-ph.CO2022

Euclid preparation. XXVII. Covariance model validation for the 2-point correlation function of galaxy clusters

Euclid Collaboration, A. Fumagalli, A. Saro +191

Aims. We validate a semi-analytical model for the covariance of real-space 2-point correlation function of galaxy clusters. Methods. Using 1000 PINOCCHIO light cones mimicking the…

astro-ph.CO2022★ 13 cited

Why weak lensing cluster shapes are insensitive to self-interacting dark matter

Andrew Robertson, Eric Huff, Katarina Markovic

We investigate whether the shapes of galaxy clusters inferred from weak gravitational lensing can be used as a test of the nature of dark matter. We analyse mock weak lensing data,…

astro-ph.GA2022★ 26 cited

Euclid preparation. XXV. The Euclid Morphology Challenge -- Towards model-fitting photometry for billions of galaxies

Euclid Collaboration, E. Merlin, M. Castellano +218

The ESA Euclid mission will provide high-quality imaging for about 1.5 billion galaxies. A software pipeline to automatically process and analyse such a huge amount of data in real…

astro-ph.IM2022

The PAU Survey & Euclid: Improving broad-band photometric redshifts with multi-task learning

L. Cabayol, M. Eriksen, J. Carretero +123

Current and future imaging surveys require photometric redshifts (photo-zs) to be estimated for millions of galaxies. Improving the photo-z quality is a major challenge but is need…

astro-ph.CO2022★ 12 cited

Euclid: Calibrating photometric redshifts with spectroscopic cross-correlations

K. Naidoo, H. Johnston, B. Joachimi +126

Cosmological constraints from key probes of the Euclid imaging survey rely critically on the accurate determination of the true redshift distributions, , of tomographic redsh…

astro-ph.GA2022

Euclid preparation: XXIII. Derivation of galaxy physical properties with deep machine learning using mock fluxes and H-band images

Euclid Collaboration, L. Bisigello, C. J. Conselice +196

Next generation telescopes, like Euclid, Rubin/LSST, and Roman, will open new windows on the Universe, allowing us to infer physical properties for tens of millions of galaxies. Ma…