Forecasting code for LSS surveys. Requires numpy, scipy, pyFFTW, and CLASS_EDE: an extension of the Boltzmann code CLASS that includes an Early Dark Energy model.
FishLSS calculates derivatives of both the 3D galaxy power spectrum and CMB lensing auto/cross spectrum, with repsect to any CLASS_EDE parameter, as well as several extenions:
(1) Primordial features (,
,
)
(2) Non-linear bias parameters (,
,
) and counterterms (
,
,
)
(3) Primordial non-Gaussinity (local through its effect on the linear bias parameter)
Here's an example showing how to create a forecasting object, calculate derivatives, and generate a Fisher matrix:
# import dependencies
from headers import *
# create CLASS object
params = {'output': 'mPk','P_k_max_h/Mpc': 40.,'non linear':'halofit',
'z_pk': '0.0,10','A_s': 2.10732e-9,'n_s': 0.96824,
'alpha_s': 0.,'h': 0.6770, 'N_ur': 1.0196,
'N_ncdm': 2,'m_ncdm': '0.01,0.05','tau_reio': 0.0568,
'omega_b': 0.02247,'omega_cdm': 0.11923,'Omega_k': 0.}
cosmo = Class()
cosmo.set(params)
cosmo.compute()
# Create and experiment, this one observes LBGs from 2 < z < 5, and we split
# the sample into three z-bins
exp = experiment(zmin=2., zmax=5., nbins=3, fsky=0.34, sigma_z=0.001, LBG=True)
# Create the forecast object.
fishcast = fisherForecast(experiment=exp,cosmo=cosmo,params=params,
khmin=5.e-4,khmax=1.,Nk=1000,Nmu=200,
velocileptors=True,name='Example')
# Specify which derivatives to compute
basis = np.array(['h','log(A_s)','n_s','omega_cdm'])
fishcast.marg_params = basis
# Compute derivatives, automatically saves to output/Example/derivatives
fishcast.compute_derivatives()
# Load derivatives and compute Fisher matrix
second_basis = np.array(['h','n_s'])
derivatives = fishcast.load_derivatives(second_basis)
F = fishcast.gen_fisher(second_basis, kmax_knl = 0.5, derivatives=derivatives)