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authFullName_s : S. M. Niemi
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Euclid preparation

R. Barnett , S. J. Warren , D. J. Mortlock , J.-G. Cuby , C. Conselice et al.
Astronomy and Astrophysics - A&A, 2019, 631, pp.A85. ⟨10.1051/0004-6361/201936427⟩
Journal articles cea-02334278v1
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Euclid preparation

H. Bretonnière , M. Huertas-Company , A. Boucaud , F. Lanusse , E. Jullo et al.
Astronomy and Astrophysics - A&A, 2022, 657, pp.A90. ⟨10.1051/0004-6361/202141393⟩
Journal articles hal-03533669v1
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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.
Astronomy and Astrophysics - A&A, 2023, 671, pp.A99. ⟨10.1051/0004-6361/202244307⟩
Journal articles hal-03866216v1

Euclid preparation. XXI. Intermediate-redshift contaminants in the search for z > 6 galaxies within the Euclid Deep Survey (Corrigendum)

S. E. van Mierlo , K. I. Caputi , M. Ashby , H. Atek , M. Bolzonella et al.
Astronomy and Astrophysics - A&A, 2022, 668, pp.C3. ⟨10.1051/0004-6361/202243950e⟩
Journal articles hal-03935710v1
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Euclid preparation

S. Ilić , N. Aghanim , C. Baccigalupi , J. R. Bermejo-Climent , G. Fabbian et al.
Astronomy and Astrophysics - A&A, 2022, 657, pp.A91. ⟨10.1051/0004-6361/202141556⟩
Journal articles hal-03533670v1
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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.
Astronomy and Astrophysics - A&A, 2022, 662, pp.A93. ⟨10.1051/0004-6361/202142419⟩
Journal articles hal-03447630v1
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Euclid: Forecasts from redshift-space distortions and the Alcock-Paczynski test with cosmic voids

N. Hamaus , M. Aubert , A. Pisani , S. Contarini , G. Verza et al.
Astronomy and Astrophysics - A&A, 2022, 658, pp.A20. ⟨10.1051/0004-6361/202142073⟩
Journal articles hal-03426564v1

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

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

Euclid preparation: IX. EuclidEmulator2 -- Power spectrum emulation with massive neutrinos and self-consistent dark energy perturbations

Euclid Collaboration , M. Knabenhans , J. Stadel , D. Potter , J. Dakin et al.
2020
Preprints, Working Papers, ... hal-03027400v1

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

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.
Monthly Notices of the Royal Astronomical Society: Letters, 2023, 520 (3), pp.3529-3548. ⟨10.1093/mnras/stac3810⟩
Journal articles insu-03947319v1
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Euclid preparation. XVII. Cosmic Dawn Survey: Spitzer Space Telescope observations of the Euclid deep fields and calibration fields

A. Moneti , H. J. Mccracken , M. Shuntov , O. B. Kauffmann , P. Capak et al.
Astronomy and Astrophysics - A&A, 2022, 658, pp.A126. ⟨10.1051/0004-6361/202142361⟩
Journal articles hal-03565435v1