CHAM: a fast algorithm of modelling non-linear matter power spectrum in the sCreened HAlo Model

Abstract

We present a fast numerical screened halo model algorithm (CHAM) for modeling non-linear power spectrum for the alternative models to LCDM. This method has three obvious advantages. First of all, it is not being restricted to a specific dark energy/modified gravity model. In principle, all of the screened scalar-tensor theories can be applied. Second, the least assumptions are made in the calculation. Hence, the physical picture is very easily understandable. Third, it is very predictable and does not rely on the calibration from N-body simulation. As an example, we show the case of Hu-Sawicki f(R) gravity. In this case, the typical CPU time with the current parallel Python script (8 threads) is roughly within $10$ minutes. The resulting spectra are in a good agreement with N-body data within a few percentage accuracy up to k~1 h/Mpc.

Python script is publicly available at https://github.com/hubinitp/CHAM

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• A general framework to test gravity using galaxy clusters II: A universal model for the halo concentration in $f(R)$ gravity
• A general framework to test gravity using galaxy clusters IV: Cluster and halo properties in DGP gravity
• Parametrizations for tests of gravity
• Non-linear matter power spectrum without screening dynamics modelling in $f(R)$ gravity