China Space Station Telescope (CSST) Emulator

A python package for CSST cosmological emulator. This package is only dependent on numpy, scipy packages. All the gaussian process trainings have been done in advance. The whole package predicts the cosmological statistics [e.g., nonlinear matter power spectrum] within $\lesssim 15$ millisecond per cosmology. The details of the training simulation suite Kun Universe are available on readthedoc.

The parameter space are shown as followed:

Parameter	Lower Limit	Upper Limit
$\Omega_b$	0.04	0.06
$\Omega_{cb}$	0.24	0.40
$H_0$	60	80
$n_s$	0.92	1.00
$A_s\times 10^{9}$	1.7	2.5
$w$	-1.3	-0.7
$w_a$	-0.5	0.5
$\sum M_{\nu}$	0	0.3

Up to now, the supportted statistics include:

1. PkLin: Linear matter power spectrum ($0\leq z \leq3$ and $10^{-5}\leq k \leq 100 {\rm\ hMpc^{-1}}$);
2. Pkmm: Matter power spectrum ($0\leq z \leq3$ and $0.00628\leq k \leq 10 {\rm\ hMpc^{-1}}$);
3. Xihm: Halo-matter cross-correlation function ($0\leq z \leq0.8$ and $10^{-2}\leq r \leq 500 {\rm\ h^{-1}Mpc}$). Now this only supports 7 fixed mass bin: [13.0, 13.2, 13.4, 13.6, 13.8, 14.0, 14.4, 15.0];
4. Ximm: Matter-matter correlation function ($0\leq z \leq3$ and $r \geq 10^{-2} {\ h^{-1}\mathrm{Mpc}}$);
5. Cell: Lensing convergence power spectrum ($0.5\leq z_s \leq3.0$);
6. comming soon ~~ :).

The accuracy for the matter power spectrum on the whole parameter space is shown as followed:

Dependence

• numpy
• scipy
• CAMB [optional]
• CLASS [optional]
• CCL [optional]

Installation

You can install this package via pip:

pip install git+https://github.com/czymh/csstemu

Usage

You can see the documentation on readthedocs for more details. Some examples are shown in the test directory.

Acknowledgements

Feel free to contact chyiru@sjtu.edu.cn if you have any questions.