Halo mass function

In this section we show how to compute the halo mass functions using the class colibri.cosmology.cosmo() We also provide alternatives to the test code. The text below refers to the file named test_mass_function.py provided in the tests directory or similarly to test_mass_function.ipynb provided in the notebooks folder.

Initialization

First of all, let us define scales, redshifts, masses and a colibri.cosmology.cosmo() instance. Also, we load the linear power spectra, which are necessary to compute mass functions and related quantities.

import colibri.cosmology as cc
import matplotlib.pyplot as plt
import numpy as np

C=cc.cosmo()

zz   = np.linspace(0., 5., 6)
kk   = np.logspace(-4.,2.,1001)
logM = np.linspace(5.,16.,111)
nz, nk, nm = len(np.atleast_1d(zz)), len(np.atleast_1d(kk)), len(np.atleast_1d(logM))

# Load linear power spectra
k,pk=C.camb_Pk(z=zz)

Mass variances, peak height and mass functions

The colibri.cosmology.cosmo() class has routines able to compute many interesting quantities for galaxy clustering. In particular, the mass variance in spheres, the peak height for the computation of peak-background split and the halo mass function itself.

sigma_squared = C.mass_variance(logM = logM, k = k, pk = pk)                                 # mass variance in spheres
nu_peak       = C.peak_height(logM = logM, k = k, pk = pk)                                   # peak-background split
ShethTormen   = C.ShethTormen_mass_function(sigma = sigma_squared**0.5, a = 0.707, p = 0.3)  # Sheth-Tormen function
HMF           = C.halo_mass_function(logM = logM, k = k, pk = pk, mass_fun = 'ShethTormen')  # halo mass function

These 4 quantities return 2D arrays (one dimension is redshift, the other is mass/peak height). If one wants to plot all the previous quantities, the following picture is the result.