15 citations · 38 across the 9 of their papers we have counts for
10 papers
Euclid Quick Data Release (Q1) Euclid spectroscopy of quasars. 2. Physical properties from spectral fitting
Euclid Collaboration, J. Calhau, G. Calderone +312
A substantial volume of spectroscopic data has become available with the Euclid Data Release (Q1), which represents a unique opportunity to study quasars (QSOs) in the Euclid Deep…
Euclid. A two-point correlation approach to diagnosing star-related systematics in the Euclid spectroscopic survey
Euclid Collaboration, I. Risso, B. R. Granett +298
The Euclid spectroscopic survey will measure galaxy clustering with unprecedented precision, requiring stringent control of observational and instrumental systematics. Star-related…
Euclid preparation. The shape of halo profiles in CDM and non-standard cosmologies
Euclid Collaboration, L. Pizzuti, G. Y. Ferron +304
We study the shape of three-dimensional and projected dark-matter halo profiles extracted from cosmological -body simulations in CDM and non-standard cosmologies, using the \…
Euclid preparation. CosmoPostProcess: A simulation calibrated framework for weak lensing selection bias in richness-selected galaxy clusters
Euclid Collaboration, R. Ingrao, M. Costanzi +280
We present \texttt{CosmoPostProcess}, a simulation-based forward-modelling algorithm calibrated to reproduce Euclid optical cluster observables. Its main deliverable is a correctio…
Euclid preparation. XCI. Validation method for the detections in the Euclid Catalogue of Galaxy Clusters using external data
Euclid Collaboration, J. -B. Melin, S. A. Stanford +315
We present our methodology for identifying known clusters as counterparts to objects in the Euclid Catalogue of Galaxy Clusters (ECGC). Euclid is expected to detect a large number…
Euclid preparation. LXXIV. Euclidised observations of Hubble Frontier Fields and CLASH galaxy clusters
Euclid Collaboration, P. Bergamini, M. Meneghetti +263
We present HST2EUCLID, a novel Python code to generate Euclid realistic mock images in the , , , and photometric bands based on panchro…
Object classification with Convolutional Neural Networks: from KiDS to Euclid
G. A. Verdoes Kleijn, C. A. Marocico, Y. Mzayek +11
Large-scale imaging surveys have grown about 1000 times faster than the number of astronomers in the last 3 decades. Using Artificial Intelligence instead of astronomer's brains fo…
Euclid preparation: XLVIII. The pre-launch Science Ground Segment simulation framework
Euclid Collaboration, S. Serrano, P. Hudelot +274
The European Space Agency's Euclid mission is one of the upcoming generation of large-scale cosmology surveys, which will map the large-scale structure in the Universe with unprece…
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: Identification of asteroid streaks in simulated images using StreakDet software
M. Pöntinen, M. Granvik, A. A. Nucita +57
The ESA Euclid space telescope could observe up to 150 000 asteroids as a side product of its primary cosmological mission. Asteroids appear as trailed sources, that is streaks, in…