Numerical implementation of the Cubic Galileon model in PINOCCHIO

TL;DR

This paper develops a fast, approximate method to simulate nonlinear galaxy clustering in the cubic Galileon modified gravity model, accurately reproducing key statistical measures compared to full N-body simulations.

Contribution

It introduces a perturbative, computationally efficient implementation of the cubic Galileon model in the PINOCCHIO code, including Vainshtein screening effects, validated against N-body simulations.

Findings

Accurately reproduces halo mass function and bias

Matches matter power spectrum trends from simulations

Significantly faster than full N-body simulations

Abstract

We present a perturbative treatment of nonlinear galaxy clustering in the context of the cubic Galileon modified gravity model, in terms of 2nd 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 realisations 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-nonlinear 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.