924 citations · 3.2k across the 132 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
Euclid: Testing the Copernican principle with next-generation surveys
D. Camarena, V. Marra, Z. Sakr +112
The Copernican principle, the notion that we are not at a special location in the Universe, is one of the cornerstones of modern cosmology and its violation would invalidate the Fr…
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…
Euclid: Forecasts from the void-lensing cross-correlation
M. Bonici, C. Carbone, S. Davini +129
The Euclid space telescope will survey a large dataset of cosmic voids traced by dense samples of galaxies. In this work we estimate its expected performance when exploiting angula…