UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Euclid: Validation of the MontePython forecasting tools

Casas, S; Lesgourgues, J; Schöneberg, N; Sabarish, VM; Rathmann, L; Doerenkamp, M; Archidiacono, M; ... Riccio, G; + view all (2024) Euclid: Validation of the MontePython forecasting tools. Astronomy and Astrophysics , 682 , Article A90. 10.1051/0004-6361/202346772. Green open access

[thumbnail of aa46772-23.pdf]
Preview
Text
aa46772-23.pdf - Published Version

Download (16MB) | Preview

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

Type: Article
Title: Euclid: Validation of the MontePython forecasting tools
Open access status: An open access version is available from UCL Discovery
DOI: 10.1051/0004-6361/202346772
Publisher version: http://dx.doi.org/10.1051/0004-6361/202346772
Language: English
Additional information: 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. This article is published in open access under the Subscribe to Open model. Subscribe to A&A to support open access publication.
Keywords: cosmology: theory – surveys – cosmology: observations – large-scale structure of Universe – cosmological parameters
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Space and Climate Physics
URI: https://discovery.ucl.ac.uk/id/eprint/10188206
Downloads since deposit
3Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item