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THE NEXT GENERATION VIRGO CLUSTER SURVEY. XV. THE PHOTOMETRIC REDSHIFT ESTIMATION FOR BACKGROUND SOURCES
A. Raichoor
,
S. Mei
,
T. Erben
,
H. Hildebrandt
,
M. Huertas-Company
et al.
Journal articles
hal-01441980v1
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Euclid preparation: XXII. Selection of Quiescent Galaxies from Mock Photometry using Machine Learning
A. Humphrey
,
L. Bisigello
,
P. A. C. Cunha
,
M. Bolzonella
,
S. Fotopoulou
et al.
Journal articles
hal-03866216v1
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RCS2 J232727.6-020437: AN EFFICIENT COSMIC ℡ESCOPE AT z=0.6986
A. Hoag
,
M. Bradac
,
K. H. Huang
,
R. E. Ryan
,
K. Sharon
et al.
Journal articles
hal-01439787v1
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Evidence of the accelerated expansion of the Universe from weak lensing tomography with COSMOS
T. Schrabback
,
J. Hartlap
,
B. Joachimi
,
M. Kilbinger
,
P. Simon
et al.
Journal articles
hal-03646149v1
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Euclid: Calibrating photometric redshifts with spectroscopic cross-correlations
K. Naidoo
,
H. Johnston
,
B. Joachimi
,
J.L. van den Busch
,
H. Hildebrandt
et al.
Astron.Astrophys., 2023, 670, pp.A149
Journal articles
hal-03772761v1
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Euclid Preparation XXIX: Forecasts for 10 different higher-order weak lensing statistics
V. Ajani
,
M. Baldi
,
A. Barthelemy
,
A. Boyle
,
P. Burger
et al.
2023
Preprints, Working Papers, ...
hal-03986111v1
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Euclid preparation. XXX. Evaluating the weak lensing cluster mass biases using the Three Hundred Project hydrodynamical simulations
C. Giocoli
,
M. Meneghetti
,
E. Rasia
,
S. Borgani
,
G. Despali
et al.
2023
Preprints, Working Papers, ...
hal-03991062v1
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Euclid preparation. XXVII. Covariance model validation for the 2-point correlation function of galaxy clusters
A. Fumagalli
,
A. Saro
,
S. Borgani
,
T. Castro
,
M. Costanzi
et al.
2022
Preprints, Working Papers, ...
hal-03892184v1
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GaBoDS: The Garching-Bonn Deep Survey. IV. Methods for the image reduction of multi-chip cameras demonstrated on data from the ESO Wide-Field Imager
T. Erben
,
M. Schirmer
,
J.P. Dietrich
,
O. Cordes
,
L. Haberzettl
et al.
Journal articles
hal-00388262v1
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CFHTLenS: the Canada-France-Hawaii Telescope Lensing Survey - imaging data and catalogue products
T. Erben
,
H. Hildebrandt
,
L. Miller
,
L. van Waerbeke
,
C. Heymans
et al.
Journal articles
hal-03645501v1
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CFHTLenS: mapping the large-scale structure with gravitational lensing
L. van Waerbeke
,
J. Benjamin
,
T. Erben
,
C. Heymans
,
H. Hildebrandt
et al.
Journal articles
hal-03645497v1
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CARS: the CFHTLS-Archive-Research Survey. I. Five-band multi-colour data from 37 sq. deg. CFHTLS-wide observations
T. Erben
,
H. Hildebrandt
,
M. Lerchster
,
P. Hudelot
,
J. Benjamin
et al.
Journal articles
hal-03646353v1
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CFHTLenS: a Gaussian likelihood is a sufficient approximation for a cosmological analysis of third-order cosmic shear statistics
P. Simon
,
E. Semboloni
,
L. van Waerbeke
,
H. Hoekstra
,
T. Erben
et al.
Journal articles
insu-03644953v1
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PHAT: PHoto-z Accuracy Testing
H. Hildebrandt
,
S. Arnouts
,
P. Capak
,
L. A. Moustakas
,
C. Wolf
et al.
Journal articles
hal-01438925v1
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Spectroscopic Needs for Imaging Dark Energy Experiments
J. Newman
,
A. Abate
,
F. Abdalla
,
S. Allam
,
S. Allen
et al.
Journal articles
in2p3-00868591v1
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Weak lensing magnification of SpARCS galaxy clusters
A. Tudorica
,
H. Hildebrandt
,
M. Tewes
,
H. Hoekstra
,
C.B. Morrison
et al.
Journal articles
hal-01669437v1
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The galaxy-halo connection from a joint lensing, clustering and abundance analysis in the CFHTLenS/VIPERS field
J. Coupon
,
S. Arnouts
,
L. Van Waerbeke
,
T. Moutard
,
O. Ilbert
et al.
Journal articles
hal-01439834v1
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Euclid preparation: XIX. Impact of magnification on photometric galaxy clustering
F. Lepori
,
I. Tutusaus
,
C. Viglione
,
C. Bonvin
,
S. Camera
et al.
Journal articles
hal-03447630v1
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Euclid preparation
V. Guglielmo
,
R. Saglia
,
F. Castander
,
A. Galametz
,
S. Paltani
et al.
Journal articles
hal-03026941v1
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3D cosmic shear: cosmology from CFHTLenS
T. D. Kitching
,
A. F. Heavens
,
J. Alsing
,
T. Erben
,
C. Heymans
et al.
Journal articles
insu-03645339v1
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Euclid preparation. XXV. The Euclid Morphology Challenge -- Towards model-fitting photometry for billions of galaxies
E. Merlin
,
M. Castellano
,
H. Bretonnière
,
M. Huertas-Company
,
U. Kuchner
et al.
Astronomy and Astrophysics - A&A, 2023, 671, pp.A101
Journal articles
hal-03841758v1
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Euclid preparation XXVI: The Euclid Morphology Challenge. Towards structural parameters for billions of galaxies
H. Bretonnière
,
U. Kuchner
,
M. Huertas-Company
,
E. Merlin
,
M. Castellano
et al.
Astronomy and Astrophysics - A&A, 2023, 671, pp.A102
Journal articles
hal-03841727v1
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Euclid preparation: XVIII. Cosmic Dawn Survey. Spitzer observations of the Euclid deep fields and calibration fields
Andrea Moneti
,
H. J. Mccracken
,
M. Shuntov
,
O. B. Kauffmann
,
P. Capak
et al.
2021
Preprints, Working Papers, ...
hal-03451656v1
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Lensing without borders – I. A blind comparison of the amplitude of galaxy–galaxy lensing between independent imaging surveys
A. Leauthaud
,
A. Amon
,
S. Singh
,
D. Gruen
,
J.U. Lange
et al.
Journal articles
hal-03603022v1
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The PAU Survey & Euclid: Improving broad-band photometric redshifts with multi-task learning
L. Cabayol
,
M. Eriksen
,
J. Carretero
,
R. Casas
,
F.J. Castander
et al.
Astron.Astrophys., 2023, 671, pp.A153
Journal articles
hal-03815511v1
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Euclid: Calibrating photometric redshifts with spectroscopic cross-correlations
K. Naidoo
,
H. Johnston
,
B. Joachimi
,
J. L. van den Busch
,
H. Hildebrandt
et al.
2022
Preprints, Working Papers, ...
hal-03866242v1
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Potential scientific synergies in weak lensing studies between the CSST and Euclid space probes
D.Z. Liu
,
X.M. Meng
,
X.Z. Er
,
Z.H. Fan
,
M. Kilbinger
et al.
Astron.Astrophys., 2023, 669, pp.A128
Journal articles
hal-03862345v1
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Bayesian galaxy shape measurement for weak lensing surveys - III. Application to the Canada-France-Hawaii Telescope Lensing Survey
L. Miller
,
C. Heymans
,
T. D. Kitching
,
L. van Waerbeke
,
T. Erben
et al.
Journal articles
hal-03645594v1
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KiDS and Euclid: Cosmological implications of a pseudo angular power spectrum analysis of KiDS-1000 cosmic shear tomography
A. Loureiro
,
L. Whittaker
,
A. Spurio Mancini
,
B. Joachimi
,
A. Cuceu
et al.
Journal articles
hal-03839670v1
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Euclid preparation: XXIII. Derivation of galaxy physical properties with deep machine learning using mock fluxes and H-band images
L. Bisigello
,
C. J. Conselice
,
M. Baes
,
M. Bolzonella
,
M. Brescia
et al.
2022
Preprints, Working Papers, ...
hal-03866380v1
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