Euclid: Forecast constraints on consistency tests of the $\Lambda$CDM model
Abstract
The standard cosmological model is based on the fundamental assumptions of a spatially homogeneous and isotropic universe on large scales. An observational detection of a violation of these assumptions at any redshift would immediately indicate the presence of new physics. We quantify the ability of the Euclid mission, together with contemporary surveys, to improve the current sensitivity of null tests of the canonical cosmological constant and the cold dark matter (LCDM) model in the redshift range . We considered both currently available data and simulated Euclid and external data products based on a LCDM fiducial model, an evolving dark energy model assuming the Chevallier-Polarski-Linder (CPL) parameterization or an inhomogeneous Lema\^{\i}tre-Tolman-Bondi model with a cosmological constant (LLTB), and carried out two separate but complementary analyses: a machine learning reconstruction of the null tests based on genetic algorithms, and a theory-agnostic parametric approach based on Taylor expansion and binning of the data, in order to avoid assumptions about any particular model. We find that in combination with external probes, Euclid can improve current constraints on null tests of the LCDM by approximately a factor of three when using the machine learning approach and by a further factor of two in the case of the parametric approach. However, we also find that in certain cases, the parametric approach may be biased against or missing some features of models far from LCDM. Our analysis highlights the importance of synergies between Euclid and other surveys. These synergies are crucial for providing tighter constraints over an extended redshift range for a plethora of different consistency tests of some of the main assumptions of the current cosmological paradigm.
Cite
@article{arxiv.2110.11421,
title = {Euclid: Forecast constraints on consistency tests of the $\Lambda$CDM model},
author = {S. Nesseris and D. Sapone and M. Martinelli and D. Camarena and V. Marra and Z. Sakr and J. Garcia-Bellido and C. J. A. P. Martins and C. Clarkson and A. Da Silva and P. Fleury and L. Lombriser and J. P. Mimoso and S. Casas and V. Pettorino and I. Tutusaus and A. Amara and N. Auricchio and C. Bodendorf and D. Bonino and E. Branchini and M. Brescia and V. Capobianco and C. Carbone 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 M. Douspis and F. Dubath and C. A. J. Duncan and X. Dupac and S. Dusini and A. Ealet and S. Farrens and P. Fosalba and M. Frailis and E. Franceschi and M. Fumana and B. Garilli and B. Gillis and C. Giocoli and A. Grazian and F. Grupp and S. V. H. Haugan and W. Holmes and F. Hormuth and K. Jahnke and S. Kermiche and A. Kiessling and T. Kitching and M. Kümmel and M. Kunz and H. Kurki-Suonio and S. Ligori and P. B. Lilje and I. Lloro and O. Mansutti and O. Marggraf and K. Markovic and F. Marulli and R. Massey 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 M. Poncet and L. Popa and G. D. Racca and F. Raison and J. Rhodes and M. Roncarelli 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 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 Y. Wang and N. Welikala and G. Zamorani and J. Zoubian and S. Andreon and M. Baldi and S. Camera and E. Medinaceli and S. Mei and A. Renzi},
journal= {arXiv preprint arXiv:2110.11421},
year = {2022}
}
Comments
23 pages, 9 figures. Changes match published version