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Numerical implementation of the Cubic Galileon model in pinocchio.

Authors :
Song, Yanling
Moretti, Chiara
Monaco, Pierluigi
Hu, Bin
Source :
Monthly Notices of the Royal Astronomical Society. Nov2022, Vol. 516 Issue 4, p5762-5774. 13p.
Publication Year :
2022

Abstract

We present a perturbative treatment of non-linear galaxy clustering in the context of the cubic Galileon modified gravity model, in terms of second-order Lagrangian Perturbation theory and an extension of ellipsoidal collapse that includes Vainshtein screening. We numerically implement such prescriptions in the approximate pinocchio code, and use it to generate realizations of the matter density field and halo catalogues with different prescriptions for ellipsoidal collapse. We investigate the impact of three different approximations in the computation of collapse times on the halo mass function, halo bias, and matter power spectrum. In the halo mass function, both the modified gravity effect and the screening effect are significant in the high-mass end, similar to what is found for other MG models. We perform a comparison with N -body simulations to assess the validity of our approach, and show that we can reproduce the same trend observed in simulations for all quantities considered. With a simple modification to the grouping algorithm of pinocchio to take into account the gravity modification, and without the need to re-calibrate the algorithm, we show that we can reproduce the linear halo bias and the mildly non-linear matter power spectrum of simulations with good accuracy, especially for the implementation with Vainshtein screening. We stress that, while approximate, our method is orders of magnitude faster than a full N -body simulation, making it an optimal tool for the quick generation of large sets of halo catalogues for cosmological observables. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00358711
Volume :
516
Issue :
4
Database :
Academic Search Index
Journal :
Monthly Notices of the Royal Astronomical Society
Publication Type :
Academic Journal
Accession number :
159622387
Full Text :
https://doi.org/10.1093/mnras/stac2298