MGCAMB v4.0

Modified Growth with CAMB

This is the official repository for the MGCAMB v4.0 patch. Below is an introduction and the instructions to install and run the code.

Table of contents

• MGCAMB v4.0
  • Modified Growth with CAMB
  • Table of contents
  • 1. Introduction
    • Structure of the code
    • Citing MGCAMB
  • 2. How to install
  • 3. How to run
  • 4. What's new
  • 5. Known issues
  • 6. Authors List

1. Introduction

Modified Growth with CAMB (MGCAMB) is a patch for the Einstein Boltzmann solver CAMB that intrdouces phenomenological Modifications of Growth (MG) along with dynamical Dark Energy (DE). It includes several phenomenological parametrizations. For instance:

• the mu, gamma parametrization, defined as

• the mu, Sigma parametrization, defined as

• the Q,R parametrization, defined as

MGCAMB is implemented in the latest version of CosmoMC and can be used with Cobaya. The MGCosmoMC and MGCobaya codes are also available for download.

Structure of the code

The new MGCAMB patch is structured as in the figure.

The parameters in params_MG.ini are used to run the code and follow the structure above. Please note that dynamical DE is supported in the pure_MG_models, where DE perturbations could also be included, and cubic-spline model.

Citing MGCAMB

If you use MGCAMB for your scientific work, please cite the following papers:

• New MGCAMB tests of gravity with CosmoMC and Cobaya
  Zhuangfei Wang, Seyed Hamidreza Mirpoorian, Levon Pogosian, Alessandra Silvestri, Gong-Bo Zhao
  arXiv:2305.05667 [astro-ph.CO]

• MGCAMB with massive neutrinos and dynamical dark energy
  Alex Zucca, Levon Pogosian, Alessandra Silvestri, and Gong-Bo Zhao
  arXiv:1901.05956 [astro-ph.CO]

• Testing Gravity with CAMB and CosmoMC
  Alireza Hojjati, Levon Pogosian, Gong-Bo Zhao,
  arXiv:1106.4543 [astro-ph.CO], JCAP 1108:005,2011

• Searching for modified growth patterns with tomographic surveys
  Gong-Bo Zhao, Levon Pogosian, Alessandra Silvestri, Joel Zylberberg,
  arXiv:0809.3791 [astro-ph], Phys. Rev. D 79, 083513

as well as the original CAMB paper and code.

2. How to install

To install MGCAMB on your machine, simply run

git clone https://github.com/sfu-cosmo/MGCAMB_v4.git
cd MGCAMB_v4/fortran/
make 

3. How to run

To run MGCAMB, first modify the params_MG.ini file according to which models you want to use. Then run

./camb ../inifiles/params.ini

4. What's new

New features with this new version of the code:

• implemented the QSA models where only dark matter is coupled to scalar field
• added background model based on parameterization of the DE density Omega_x, as opposed to w
• implemented a non-parametric reconstruction of mu, Sigma and Omega_x based on cubic-spline interpolation
• implemented the direct mu, Sigma parametrization for different models
• the option of including DE perturbations to achieve the consistency with CAMB when working with $w \neq −1$ background models. (controlled by parameter MGDE_pert)
• added a python wrapper to run MGCAMB from the python interface
  (make sure to use function set_mgparams alongside other functions e.g. set_cosmology or set_params in your script.)
• the code has been restructured and upgraded to CAMB version 1.3.5
• added compatibility with Cobaya (MGCobaya)

The MG and DE parametrizations along with the computation of the quantities related to the perturbations are introduced in the file mgcamb.f90.

5. Known issues

• The debug mode (make Debug) is not well-tested for this version. Please run the code using the normal option described in sections 2 and 3 .
• (Fixed) The setting of Dark energy interface and the reading issue of X_arr for DE_model == 3 in Fortran core. Thanks to Kushal Lodha.
• The model of effective Newton's constant (mugamma_par = 3) is not fully developed.

6. Authors List

Main Developers:

• Zhuangfei Wang (Email: zhuangfei_wang@sfu.ca)
• Alex Zucca (Email: alexzucca90@gmail.com)

Original Code Developers:

• Gong-Bo Zhao
• Alireza Hojjati
• Levon Pogosian
• Alessandra Silvestri

Repo created and maintained by Zhuangfei Wang. If you find any bugs in the code, please contact Zhuangfei Wang at zhuangfei_wang@sfu.ca .