CLASS-PT: nonlinear perturbation theory extension of the Boltzmann code CLASS

This is a modification of the CLASS code that computes the non-linear power spectra of dark matter and biased tracers in one-loop cosmological perturbation theory, for both Gaussian and non-Gaussian initial conditions.

CLASS-PT can be interfaced with the MCMC sampler MontePython using the (new and improved) custom-built likelihoods found here.

The code is compatible with both python2 and python3.

Getting started

Read these instructions for the installation details. See also this troubleshooting guide.

The installation instuctions for CLASS can be found on the official code webpage.

Once you are all set, check out this jupyter notebook for the examples of working sessions. Here's a simple example of computing the galaxy power spectrum multipoles with CLASS-PT:

# Import modules
from classy import Class
import numpy as np

# Set usual CLASS parameters
z_pk = 0.5
cosmo = Class()
cosmo.set({'A_s':2.089e-9,
           'n_s':0.9649,
           'tau_reio':0.052,
           'omega_b':0.02237,
           'omega_cdm':0.12,
           'h':0.6736,
           'YHe':0.2425,
           'N_ur':2.0328,
           'N_ncdm':1,
           'm_ncdm':0.06,
           'z_pk':z_pk
          })  
# Set additional CLASS-PT settings
cosmo.set({'output':'mPk',
           'non linear':'PT',
           'IR resummation':'Yes',
           'Bias tracers':'Yes',
           'cb':'Yes', # use CDM+baryon spectra
           'RSD':'Yes',
           'AP':'Yes', # Alcock-Paczynski effect
           'Omfid':'0.31', # fiducial Omega_m
           'PNG':'No' # single-field inflation PNG
         })
cosmo.compute()

# Define some wavenumbers and compute spectra
khvec = np.logspace(-3,np.log10(1),1000) # array of k in 1/Mpc
cosmo.initialize_output(khvec, z_pk, len(khvec))

# Define nuisance parameters and extract outputs
b1, b2, bG2, bGamma3, cs0, cs2, cs4, Pshot, b4 = 2., -1., 0.1, -0.1, 0., 30., 0., 3000., 10.
pk_g0 = cosmo.pk_gg_l0(b1, b2, bG2, bGamma3, cs0, Pshot, b4)
pk_g2 = cosmo.pk_gg_l2(b1, b2, bG2, bGamma3, cs2, b4)
pk_g4 = cosmo.pk_gg_l4(b1, b2, bG2, bGamma3, cs4, b4)

You can also use the Mathematica notebook 'read_tables.nb' to read the code output. We also provide a technical summary of the fNL implementations here.

Using the code

You can use CLASS-PT freely, provided that in your publications you cite at least the code paper arXiv:2004.10607. Feel free to cite the other companion papers devoted to new large-scale structure analysis methodologies!

Authors

• Mikhail (Misha) Ivanov
• Anton Chudaykin
• Marko Simonovic
• Oliver Philcox
• Giovanni Cabass