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Euclid: Validation of the MontePython forecasting tools

Casas, S.; Lesgourgues, J.; Schöneberg, N.; Sabarish, V. M.; Rathmann, L.; Doerenkamp, M.; Archidiacono, M.; Bellini, E.; Clesse, S.; Frusciante, N.; Martinelli, M.; Pace, F.; Sapone, D.; Sakr, Z.; Blanchard, A.; Brinckmann, T.; Camera, S.; Carbone, C.; Ilić, S.; Markovic, K.; Pettorino, V.; Tutusaus, I.; Aghanim, N.; Amara, A.; Amendola, L.; Auricchio, N.; Baldi, M.; Bonino, D.; Branchini, E.; Brescia, M.; Brinchmann, J.; Capobianco, V.; Cardone, V. F.; Carretero, J.; Castellano, M.; Cavuoti, S.; Cimatti, A.; Cledassou, R.; Congedo, G.; Conversi, L.; Copin, Y.; Corcione, L.; Courbin, F.; Cropper, M.; Degaudenzi, H.; Dinis, J.; Douspis, M.; Dubath, F.; Dupac, X.; Dusini, S.; Farrens, S.; Frailis, M.; Franceschi, E.; Fumana, M.; Galeotta, S.; Garilli, B.; Gillis, B.; Giocoli, C.; Grazian, A.; Grupp, F.; Guzzo, L.; Haugan, S. V. H.; Hormuth, F.; Hornstrup, A.; Jahnke, K.; Kümmel, M.; Kiessling, A.; Kilbinger, M.; Kitching, T.; Kunz, M.; Kurki-Suonio, H.; Ligori, S.; Lilje, P. B.; Lloro,...

Authors

S. Casas

J. Lesgourgues

N. Schöneberg

V. M. Sabarish

L. Rathmann

M. Doerenkamp

M. Archidiacono

E. Bellini

S. Clesse

N. Frusciante

M. Martinelli

F. Pace

D. Sapone

Z. Sakr

A. Blanchard

T. Brinckmann

S. Camera

C. Carbone

S. Ilić

K. Markovic

V. Pettorino

I. Tutusaus

N. Aghanim

A. Amara

L. Amendola

N. Auricchio

M. Baldi

D. Bonino

E. Branchini

M. Brescia

J. Brinchmann

V. Capobianco

V. F. Cardone

J. Carretero

M. Castellano

S. Cavuoti

A. Cimatti

R. Cledassou

G. Congedo

L. Conversi

Y. Copin

L. Corcione

F. Courbin

M. Cropper

H. Degaudenzi

J. Dinis

M. Douspis

F. Dubath

X. Dupac

S. Dusini

S. Farrens

M. Frailis

E. Franceschi

M. Fumana

S. Galeotta

B. Garilli

B. Gillis

C. Giocoli

A. Grazian

F. Grupp

L. Guzzo

S. V. H. Haugan

F. Hormuth

A. Hornstrup

K. Jahnke

M. Kümmel

A. Kiessling

M. Kilbinger

T. Kitching

M. Kunz

H. Kurki-Suonio

S. Ligori

P. B. Lilje

I. Lloro

O. Mansutti

O. Marggraf

F. Marulli

E. Medinaceli

S. Mei

M. Meneghetti

E. Merlin

G. Meylan

M. Moresco

L. Moscardini

E. Munari

S.-M. Niemi

C. Padilla

S. Paltani

F. Pasian

K. Pedersen

W. J. Percival

S. Pires

G. Polenta

M. Poncet

L. A. Popa

F. Raison

A. Renzi

J. Rhodes

G. Riccio

E. Romelli

M. Roncarelli

E. Rossetti

R. Saglia

B. Sartoris

P. Schneider

A. Secroun

G. Seidel

S. Serrano

C. Sirignano

G. Sirri

L. Stanco

J.-L. Starck

C. Surace

P. Tallada-Crespí

A. N. Taylor

I. Tereno

R. Toledo-Moreo

F. Torradeflot

E. A. Valentijn

L. Valenziano

T. Vassallo

Y. Wang

J. Weller

G. Zamorani

J. Zoubian

V. Scottez

A. Veropalumbo



Abstract

Context. The Euclid mission of the European Space Agency will perform a survey of weak lensing cosmic shear and galaxy clustering in order to constrain cosmological models and fundamental physics.

Aims. We expand and adjust the mock Euclid likelihoods of the MontePython software in order to match the exact recipes used in previous Euclid Fisher matrix forecasts for several probes: weak lensing cosmic shear, photometric galaxy clustering, the cross-correlation between the latter observables, and spectroscopic galaxy clustering. We also establish which precision settings are required when running the Einstein–Boltzmann solvers CLASS and CAMB in the context of Euclid.

Methods. For the minimal cosmological model, extended to include dynamical dark energy, we perform Fisher matrix forecasts based directly on a numerical evaluation of second derivatives of the likelihood with respect to model parameters. We compare our results with those of previously validated Fisher codes using an independent method based on first derivatives of the Euclid observables.

Results. We show that such MontePython forecasts agree very well with previous Fisher forecasts published by the Euclid Collab oration, and also, with new forecasts produced by the CosmicFish code, now interfaced directly with the two Einstein–Boltzmann solvers CAMB and CLASS. Moreover, to establish the validity of the Gaussian approximation, we show that the Fisher matrix marginal error contours coincide with the credible regions obtained when running Monte Carlo Markov chains with MontePython while using the exact same mock likelihoods.

Conclusions. The new Euclid forecast pipelines presented here are ready for use with additional cosmological parameters, in order to explore extended cosmological models.

Citation

Casas, S., Lesgourgues, J., Schöneberg, N., Sabarish, V. M., Rathmann, L., Doerenkamp, M., …Veropalumbo, A. (2024). Euclid: Validation of the MontePython forecasting tools. Astronomy & Astrophysics, 682, Article A90. https://doi.org/10.1051/0004-6361/202346772

Journal Article Type Article
Acceptance Date Aug 6, 2023
Online Publication Date Feb 8, 2024
Publication Date 2024-02
Deposit Date May 22, 2024
Publicly Available Date May 22, 2024
Journal Astronomy & Astrophysics
Print ISSN 0004-6361
Electronic ISSN 1432-0746
Publisher EDP Sciences
Peer Reviewed Peer Reviewed
Volume 682
Article Number A90
DOI https://doi.org/10.1051/0004-6361/202346772
Public URL https://durham-repository.worktribe.com/output/2456449

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Copyright Statement
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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