BCemu 1.1.1

Using emulators to implement baryonic effects.

BCemu

A python package for modelling baryonic effects in cosmological simulations.

Package details

The package provides emulators to model the suppression in the power spectrum due to baryonic feedback processes. These emulators are based on the baryonification model [1], where gravity-only N-body simulation results are manipulated to include the impact of baryonic feedback processes. For detailed description, see Ref. [2].

INSTALLATION

One can install a stable version of this package using pip by running the following command::

pip install BCemu

In order to use the latest version, one can clone this package running the following::

git clone https://github.com/sambit-giri/BCemu.git

To install the package in the standard location, run the following in the root directory::

python setup.py install

In order to install it in a separate directory::

python setup.py install --home=directory

One can also install it using pip by running the following command::

pip install git+https://github.com/sambit-giri/BCemu.git

The dependencies should be installed automatically during the installation process. If they fail for some reason, you can install them manually before installing BCemu. The list of required packages can be found in the requirements.txt file present in the root directory.

Tests

For testing, one can use pytest or nosetests. Both packages can be installed using pip. To run all the test script, run the either of the following::

python -m pytest tests

nosetests -v

USAGE

Script to get the baryonic power suppression.

import numpy as np 
import matplotlib.pyplot as plt
import BCemu

bfcemu = BCemu.BCM_7param(Ob=0.05, Om=0.27)
bcmdict = {'log10Mc': 13.32,
           'mu'     : 0.93,
           'thej'   : 4.235,  
           'gamma'  : 2.25,
           'delta'  : 6.40,
           'eta'    : 0.15,
           'deta'   : 0.14,
           }

z = 0
k_eval = 10**np.linspace(-1,1.08,50)
p_eval = bfcemu.get_boost(z, bcmdict, k_eval)

plt.semilogx(k_eval, p_eval, c='C0', lw=3)
plt.axis([1e-1,12,0.73,1.04])
plt.yticks(fontsize=14)
plt.xticks(fontsize=14)
plt.xlabel(r'$k$ (h/Mpc)', fontsize=14)
plt.ylabel(r'$\frac{P_{\rm DM+baryon}}{P_{\rm DM}}$', fontsize=21)
plt.tight_layout()
plt.show()

The package also has a three parameter barynification model. Model A assumes all the three parameters to be independent of redshift while model B assumes the parameters to be redshift dependent via the following form:

.

Below an example fit to the BAHAMAS simulation result is shown.

import numpy as np 
import matplotlib.pyplot as plt
import BCemu
import pickle

BAH = pickle.load(open('examples/BAHAMAS_data.pkl', 'rb'))

bfcemu = BCemu.BCM_3param(Ob=0.0463, Om=0.2793)
bcmdict = {'log10Mc': 13.25, 
           'thej'   : 4.711,  
           'deta'   : 0.097}

zs = [0,0.5]
k_eval  = 10**np.linspace(-1,1.08,50)
p0_eval1 = bfcemu.get_boost(zs[0], bcmdict, k_eval)
p1_eval1 = bfcemu.get_boost(zs[1], bcmdict, k_eval)

bfcemu = BCemu.BCM_3param(Ob=0.0463, Om=0.2793)
bcmdict = {'log10Mc': 13.25, 
           'thej'   : 4.711,  
           'deta'   : 0.097,
           'nu_Mc'  : 0.038,
           'nu_thej': 0.0,
           'nu_deta': 0.060}

zs = [0,0.5]
k_eval  = 10**np.linspace(-1,1.08,50)
p0_eval2 = bfcemu.get_boost(zs[0], bcmdict, k_eval)
p1_eval2 = bfcemu.get_boost(zs[1], bcmdict, k_eval)

plt.figure(figsize=(10,4.5))
plt.subplot(121); plt.title('z=0')
plt.semilogx(BAH['z=0']['k'], BAH['z=0']['S'], '-', c='k', lw=5, alpha=0.2, label='BAHAMAS')
plt.semilogx(k_eval, p0_eval1, c='C0', lw=3, label='A', ls='--')
plt.semilogx(k_eval, p0_eval1, c='C2', lw=3, label='B', ls=':')
plt.axis([1e-1,12,0.73,1.04])
plt.yticks(fontsize=14)
plt.xticks(fontsize=14)
plt.legend()
plt.xlabel(r'$k$ (h/Mpc)', fontsize=14)
plt.ylabel(r'$\frac{P_{\rm DM+baryon}}{P_{\rm DM}}$', fontsize=21)
plt.subplot(122); plt.title('z=0.5')
plt.semilogx(BAH['z=0.5']['k'], BAH['z=0.5']['S'], '-', c='k', lw=5, alpha=0.2, label='BAHAMAS')
plt.semilogx(k_eval, p1_eval1, c='C0', lw=3, label='A', ls='--')
plt.semilogx(k_eval, p1_eval2, c='C2', lw=3, label='B', ls=':')
plt.axis([1e-1,12,0.73,1.04])
plt.yticks(fontsize=14)
plt.xticks(fontsize=14)
plt.xlabel(r'$k$ (h/Mpc)', fontsize=14)
plt.ylabel(r'$\frac{P_{\rm DM+baryon}}{P_{\rm DM}}$', fontsize=21)
plt.tight_layout()
plt.show()

CONTRIBUTING

If you find any bugs or unexpected behavior in the code, please feel free to open a Github issue. The issue page is also good if you seek help or have suggestions for us.

References

[1] Schneider, A., Teyssier, R., Stadel, J., Chisari, N. E., Le Brun, A. M., Amara, A., & Refregier, A. (2019). Quantifying baryon effects on the matter power spectrum and the weak lensing shear correlation. Journal of Cosmology and Astroparticle Physics, 2019(03), 020. arXiv:1810.08629.

[2] Giri, S. K. & Schneider, A. (2021). Emulation of baryonic effects on the matter power spectrum and constraints from galaxy cluster data. Journal of Cosmology and Astroparticle Physics, 2021(12), 046. arXiv:2108.08863.