924 citations · 2.8k across the 84 of their papers we have counts for
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Euclid preparation. Estimating galaxy physical properties using CatBoost chained regressors with attention
Euclid Collaboration, A. Humphrey, P. A. C. Cunha +232
Euclid will image ~14000 deg^2 of the extragalactic sky at visible and NIR wavelengths, providing a dataset of unprecedented size and richness that will facilitate a multitude of s…
Euclid preparation. XLIX. Selecting active galactic nuclei using observed colours
Euclid Collaboration, L. Bisigello, M. Massimo +244
Euclid will cover over 14000 with two optical and near-infrared spectro-photometric instruments, and is expected to detect around ten million active galactic nuclei (AGN)…
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: Early Release Observations -- Unveiling the morphology of two Milky Way globular clusters out to their periphery
D. Massari, E. Dalessandro, D. Erkal +158
As part of the Euclid Early Release Observations (ERO) programme, we analyse deep, wide-field imaging from the VIS and NISP instruments of two Milky Way globular clusters (GCs), na…
Euclid preparation. Optical emission-line predictions of intermediate-z galaxy populations in GAEA for the Euclid Deep and Wide Surveys
Euclid Collaboration, L. Scharré, M. Hirschmann +239
In anticipation of the Euclid Wide and Deep Surveys, we present optical emission-line predictions at intermediate redshifts from 0.4 to 2.5. Our approach combines a mock light cone…
Euclid preparation. XLIII. Measuring detailed galaxy morphologies for Euclid with machine learning
Euclid Collaboration, B. Aussel, S. Kruk +255
The Euclid mission is expected to image millions of galaxies with high resolution, providing an extensive dataset to study galaxy evolution. We investigate the application of deep…
Euclid preparation. Spectroscopy of active galactic nuclei with NISP
Euclid Collaboration, E. Lusso, S. Fotopoulou +246
The statistical distribution and evolution of key properties (e.g. accretion rate, mass, or spin) of active galactic nuclei (AGN), remain an open debate in astrophysics. The ESA Eu…
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. XXVII. A UV-NIR spectral atlas of compact planetary nebulae for wavelength calibration
Euclid Collaboration, K. Paterson, M. Schirmer +201
The Euclid mission will conduct an extragalactic survey over 15000 deg of the extragalactic sky. The spectroscopic channel of the Near-Infrared Spectrometer and Photometer (NIS…
Euclid preparation. XXX. Performance assessment of the NISP Red-Grism through spectroscopic simulations for the Wide and Deep surveys
Euclid Collaboration, L. Gabarra, C. Mancini +232
This work focuses on the pilot run of a simulation campaign aimed at investigating the spectroscopic capabilities of the Euclid Near-Infrared Spectrometer and Photometer (NISP), in…
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…
Euclid preparation: XVIII. Cosmic Dawn Survey. Spitzer observations of the Euclid deep fields and calibration fields
Andrea Moneti, H. J. McCracken, M. Shuntov +194
We present a new infrared survey covering the three Euclid deep fields and four other Euclid calibration fields using Spitzer's Infrared Array Camera (IRAC). We have combined these…
Euclid preparation: XIII. Forecasts for galaxy morphology with the Euclid Survey using Deep Generative Models
Euclid Collaboration, H. Bretonnière, M. Huertas-Company +193
We present a machine learning framework to simulate realistic galaxies for the Euclid Survey. The proposed method combines a control on galaxy shape parameters offered by analytic…
Euclid preparation: X. The Euclid photometric-redshift challenge
Euclid Collaboration, G. Desprez, S. Paltani +170
Forthcoming large photometric surveys for cosmology require precise and accurate photometric redshift (photo-z) measurements for the success of their main science objectives. Howev…
Euclid preparation: VIII. The Complete Calibration of the Colour-Redshift Relation survey: VLT/KMOS observations and data release
Euclid Collaboration, V. Guglielmo, R. Saglia +145
The Complete Calibration of the Colour-Redshift Relation survey (C3R2) is a spectroscopic effort involving ESO and Keck facilities designed to empirically calibrate the galaxy colo…
Euclid: The selection of quiescent and star-forming galaxies using observed colours
L. Bisigello, U. Kuchner, C. J. Conselice +71
The Euclid mission will observe well over a billion galaxies out to and beyond. This will offer an unrivalled opportunity to investigate several key questions for understa…
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…
The 0.1<z<1.65 evolution of the bright end of the [OII] luminosity function
Johan Comparat, Johan Richard, Jean-Paul Kneib +33
We present the [OII] luminosity function measured in the redshift range 0.1<z<1.65 with unprecedented depth and accuracy. Our measurements are based on medium resolution flux-calib…