6.6k citations · 13.6k across the 59 of their papers we have counts for
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Deep Learning for space-variant deconvolution in galaxy surveys
Florent Sureau, Alexis Lechat, Jean-Luc Starck
Deconvolution of large survey images with millions of galaxies requires to develop a new generation of methods which can take into account a space variant Point Spread Function (PS…
Euclid preparation: VI. Verifying the Performance of Cosmic Shear Experiments
Euclid Collaboration, P. Paykari, T. D. Kitching +128
Our aim is to quantify the impact of systematic effects on the inference of cosmological parameters from cosmic shear. We present an end-to-end approach that introduces sources of…
Euclid preparation: VII. Forecast validation for Euclid cosmological probes
Euclid Collaboration, A. Blanchard, S. Camera +151
The Euclid space telescope will measure the shapes and redshifts of galaxies to reconstruct the expansion history of the Universe and the growth of cosmic structures. Estimation of…
Euclid preparation: V. Predicted yield of redshift 7<z<9 quasars from the wide survey
Euclid Collaboration, R. Barnett, S. J. Warren +126
We provide predictions of the yield of quasars from the Euclid wide survey, updating the calculation presented in the Euclid Red Book in several ways. We account for revisi…
Representation learning for automated spectroscopic redshift estimation
Joana Frontera-Pons, Florent Sureau, Bruno Moraes +2
Determining the radial positions of galaxies up to a high accuracy depends on the correct identification of salient features in their spectra. Classical techniques for spectroscopi…