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authFullName_s : P. Monaco

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

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
Image document

Euclid preparation: XXI. Intermediate-redshift contaminants in the search for $z>6$ galaxies within the Euclid Deep Survey

S. van Mierlo , K. Caputi , M. Ashby , H. Atek , M. Bolzonella et al.
Astronomy and Astrophysics - A&A, 2022, 666, pp.A200. ⟨10.1051/0004-6361/202243950⟩
Journal articles hal-03866172v1
Image document

The Herschel* PEP/HerMES luminosity function - I. Probing the evolution of PACS selected Galaxies to z similar or equal to 4

C. Gruppioni , F. Pozzi , G. Rodighiero , I. Delvecchio , S. Berta et al.
Monthly Notices of the Royal Astronomical Society, 2013, 432 (1), pp.23--52. ⟨10.1093/mnras/stt308⟩
Journal articles hal-01439508v1

Erratum: The Herschel PEP/HerMES Luminosity Function - I. Probing the Evolution of PACS selected Galaxies to z ≃ 4

C. Gruppioni , F. Pozzi , G. Rodighiero , I. Delvecchio , S. Berta et al.
2013, pp.2875-2876. ⟨10.1093/mnras/stt1748⟩
Other publications hal-03645432v1

Euclid: Modelling massive neutrinos in cosmology – a code comparison

J. Adamek , R.E. Angulo , C. Arnold , M. Baldi , M. Biagetti et al.
2022
Preprints, Working Papers, ... hal-03892198v1

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
Image document

Euclid : Effects of sample covariance on the number counts of galaxy clusters

A. Fumagalli , A. Saro , S. Borgani , T. Castro , M. Costanzi et al.
Astron.Astrophys., 2021, 652, pp.A21. ⟨10.1051/0004-6361/202140592⟩
Journal articles hal-03210469v1

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
Image document

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

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

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

Euclid preparation - XVIII. The NISP photometric system

M. Schirmer , K. Jahnke , G. Seidel , H. Aussel , C. Bodendorf et al.
Astron.Astrophys., 2022, 662, pp.A92. ⟨10.1051/0004-6361/202142897⟩
Journal articles hal-03613681v1

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
Image document

Different star formation laws for disks versus starbursts at low and high redshifts

Emanuele Daddi , D. Elbaz , F. Walter , F. Bournaud , F. Salmi et al.
The Astrophysical journal letters, 2010, 714 (1), pp.L118. ⟨10.1088/2041-8205/714/1/L118⟩
Journal articles cea-01004494v1

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

Tracing the evolution of dust obscured star-formation and accretion back to the reionisation epoch with SPICA

C. Gruppioni , L. Ciesla , E. Hatziminaoglou , F. Pozzi , G. Rodighiero et al.
Publications of the Astronomical Society of Australia, 2017, 34, pp.id.e055. ⟨10.1017/pasa.2017.49⟩
Journal articles hal-01612984v1

The VIMOS Public Extragalactic Redshift Survey (VIPERS) : Galaxy clustering and redshift-space distortions at z ≃ 0.8 in the first data release

S. de La Torre , L. Guzzo , J. A. Peacock , E. Branchini , A. Iovino et al.
Astronomy and Astrophysics - A&A, 2013, 557, pp.A54. ⟨10.1051/0004-6361/201321463⟩
Journal articles hal-01113744v1

Euclid preparation. XXIV. Calibration of the halo mass function in $\Lambda(\nu)$CDM cosmologies

T. Castro , A. Fumagalli , R.E. Angulo , S. Bocquet , S. Borgani et al.
Astron.Astrophys., 2023, 671, pp.A100
Journal articles hal-03778228v1

Euclid: Fast two-point correlation function covariance through linear construction

E. Keihanen , V. Lindholm , P. Monaco , L. Blot , C. Carbone et al.
Astron.Astrophys., 2022, 666, pp.A129
Journal articles hal-03691688v1

Euclid preparation: XXVIII. Modelling of the weak lensing angular power spectrum

A.C. Deshpande , T. Kitching , A. Hall , M.L. Brown , N. Aghanim et al.
2023
Preprints, Working Papers, ... hal-03998732v1