most citedEuclid: Early Release Observations -- Programme overview and pipeline for compact- and diffuse-emission photometry

45 citations

6 papers

astro-ph.GA2024★ 20 cited

Euclid: Early Release Observations -- Overview of the Perseus cluster and analysis of its luminosity and stellar mass functions

J. -C. Cuillandre, M. Bolzonella, A. Boselli +199

The Euclid ERO programme targeted the Perseus cluster of galaxies, gathering deep data in the central region of the cluster over 0.7 square degree, corresponding to approximately 0…

astro-ph.GA2024★ 23 cited

Euclid: Early Release Observations -- Globular clusters in the Fornax galaxy cluster, from dwarf galaxies to the intracluster field

T. Saifollahi, K. Voggel, A. Lançon +196

We present an analysis of Euclid observations of a 0.5 deg field in the central region of the Fornax galaxy cluster that were acquired during the performance verification phase…

astro-ph.GA2024★ 30 cited

Euclid: Early Release Observations -- Deep anatomy of nearby galaxies

L. K. Hunt, F. Annibali, J. -C. Cuillandre +196

Euclid is poised to make significant advances in the study of nearby galaxies in the local Universe. Here we present a first look at 6 galaxies observed for the Nearby Galaxy Showc…

astro-ph.IM2024★ 45 cited

Euclid: Early Release Observations -- Programme overview and pipeline for compact- and diffuse-emission photometry

J. -C. Cuillandre, E. Bertin, M. Bolzonella +260

The Euclid ERO showcase Euclid's capabilities in advance of its main mission, targeting 17 astronomical objects, from galaxy clusters, nearby galaxies, globular clusters, to star-f…

cs.CV2018★ 4 cited

Computing the Spatial Probability of Inclusion inside Partial Contours for Computer Vision Applications

Dominique Beaini, Sofiane Achiche, Fabrice Nonez +1

In Computer Vision, edge detection is one of the favored approaches for feature and object detection in images since it provides information about their objects boundaries. Other r…

stat.ML2018★ 14 cited

Generalization in Machine Learning via Analytical Learning Theory

Kenji Kawaguchi, Yoshua Bengio, Vikas Verma +1

This paper introduces a novel measure-theoretic theory for machine learning that does not require statistical assumptions. Based on this theory, a new regularization method in deep…