103 citations · 625 across the 40 of their papers we have counts for
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Euclid Quick Data Release (Q1) -- Data release overview
Euclid Collaboration, H. Aussel, I. Tereno +407
The first Euclid Quick Data Release, Q1, comprises 63.1 sq deg of the Euclid Deep Fields (EDFs) to nominal wide-survey depth. It encompasses visible and near-infrared space-based i…
Euclid preparation. The Cosmic Dawn Survey (DAWN) of the Euclid Deep and Auxiliary Fields
Euclid Collaboration, C. J. R. McPartland, L. Zalesky +288
Euclid will provide deep NIR imaging to 26.5 AB magnitude over 59 deg in its deep and auxiliary fields. The Cosmic DAWN survey complements the deep Euclid data with…
Euclid Preparation. Cosmic Dawn Survey: Data release 1 multiwavelength catalogues for Euclid Deep Field North and Euclid Deep Field Fornax
Euclid Collaboration, L. Zalesky, C. J. R. McPartland +272
The Cosmic Dawn Survey (DAWN survey) provides multiwavelength (UV/optical to mid-IR) data across the combined 59 deg of the Euclid Deep and Auxiliary fields (EDFs and EAFs).…
Euclid preparation. LVIII. Detecting globular clusters in the Euclid survey
Euclid Collaboration, K. Voggel, A. Lançon +270
Extragalactic globular clusters (EGCs) are an abundant and powerful tracer of galaxy dynamics and formation, and their own formation and evolution is also a matter of extensive deb…
Euclid Preparation XXXIII. Characterization of convolutional neural networks for the identification of galaxy-galaxy strong lensing events
Euclid Collaboration, L. Leuzzi, M. Meneghetti +216
Forthcoming imaging surveys will potentially increase the number of known galaxy-scale strong lenses by several orders of magnitude. For this to happen, images of tens of millions…
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…
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…