Euclid: Validation of the MontePython forecasting tools
Abstract
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. 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. 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 other forecasting methods and tools. We show that such MontePython forecasts agree very well with previous Fisher forecasts published by the Euclid Collaboration, 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. The new Euclid forecast pipelines presented here are ready for use with additional cosmological parameters, in order to explore extended cosmological models.
Cite
@article{arxiv.2303.09451,
title = {Euclid: Validation of the MontePython forecasting tools},
author = {S. Casas and J. Lesgourgues and N. Schöneberg and Sabarish V. M. and L. Rathmann and M. Doerenkamp and M. Archidiacono and E. Bellini and S. Clesse and N. Frusciante and M. Martinelli and F. Pace and D. Sapone and Z. Sakr and A. Blanchard and T. Brinckmann and S. Camera and C. Carbone and S. Ilić and K. Markovic and V. Pettorino and I. Tutusaus and N. Aghanim and A. Amara and L. Amendola and N. Auricchio and M. Baldi and D. Bonino and E. Branchini and M. Brescia and J. Brinchmann and V. Capobianco and V. F. Cardone and J. Carretero and M. Castellano and S. Cavuoti and A. Cimatti and R. Cledassou and G. Congedo and L. Conversi and Y. Copin and L. Corcione and F. Courbin and M. Cropper and H. Degaudenzi and J. Dinis and M. Douspis and F. Dubath and X. Dupac and S. Dusini and S. Farrens and M. Frailis and E. Franceschi and M. Fumana and S. Galeotta and B. Garilli and B. Gillis and C. Giocoli and A. Grazian and F. Grupp and S. V. H. Haugan and F. Hormuth and A. Hornstrup and K. Jahnke and M. Kümmel and A. Kiessling and M. Kilbinger and T. Kitching and M. Kunz and H. Kurki-Suonio and S. Ligori and P. B. Lilje and I. Lloro and O. Mansutti and O. Marggraf and F. Marulli and R. Massey and E. Medinaceli and S. Mei and M. Meneghetti and E. Merlin and G. Meylan and M. Moresco and L. Moscardini and E. Munari and S. -M. Niemi and C. Padilla and S. Paltani and F. Pasian and K. Pedersen and W. J. Percival and S. Pires and G. Polenta and M. Poncet and L. A. Popa and F. Raison and A. Renzi and J. Rhodes and G. Riccio and E. Romelli and M. Roncarelli and E. Rossetti and R. Saglia and B. Sartoris and P. Schneider and A. Secroun and G. Seidel and S. Serrano and C. Sirignano and G. Sirri and L. Stanco and J. -L. Starck and C. Surace and P. Tallada-Crespí and A. N. Taylor and I. Tereno and R. Toledo-Moreo and F. Torradeflot and E. A. Valentijn and L. Valenziano and T. Vassallo and Y. Wang and J. Weller and G. Zamorani and J. Zoubian and V. Scottez and A. Veropalumbo},
journal= {arXiv preprint arXiv:2303.09451},
year = {2023}
}
Comments
45 pages, 24 figures