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

TL;DR

CHAM is a fast, model-agnostic algorithm for non-linear matter power spectrum modeling in screened scalar-tensor theories, offering high accuracy without reliance on N-body calibration, exemplified by Hu-Sawicki f(R) gravity.

Contribution

It introduces a rapid, assumption-light numerical method applicable to various screened scalar-tensor models, avoiding N-body calibration.

Findings

Achieves ~3% accuracy up to k~1 h/Mpc

Computes spectra within 10 minutes using 8 CPU threads

Applicable to multiple dark energy and modified gravity models

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.