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base.param
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base.param
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#------Experiments to test (separated with commas)-----
########################### WARNING ####################################
# This file was showing how to run the base model with Planck 2013 data.
# We leave this file for reference.
# But please use the file base2015.param for Planck 2015!
# The nuisance parameter names and priors have changed!
########################################################################
data.experiments=['Planck_highl','Planck_lowl','lowlike']
#------ Settings for the over-sampling.
# The first element will always be set to 1, for it is the sampling of the
# cosmological parameters. The other numbers describe the over sampling of the
# nuisance parameter space. This array must have the same dimension as the
# number of blocks in your run (so, 1 for cosmological parameters, and then 1
# for each experiment with varying nuisance parameters).
# Note that when using Planck likelihoods, you definitely want to use [1, 4],
# to oversample as much as possible the 14 nuisance parameters.
# Remember to order manually the experiments from slowest to fastest (putting
# Planck as the first set of experiments should be a safe bet, except if you
# also have LSS experiments).
# If you have experiments without nuisance, you do not need to specify an
# additional entry in the over_sampling list (notice for instance that, out of
# the three Planck likelihoods used, only Planck_highl requires nuisance
# parameters, therefore over_sampling has a length of two (cosmology, plus one
# set of nuisance).
data.over_sampling=[1, 4]
#------ Parameter list -------
# data.parameters[class name] = [mean, min, max, 1-sigma, scale, role]
# - if min max irrelevant, put to None
# - if fixed, put 1-sigma to 0
# - if scale irrelevant, put to 1, otherwise to the appropriate factor
# - role is either 'cosmo', 'nuisance' or 'derived'. You should put the derived
# parameters at the end, and in case you are using the `-j fast` Cholesky
# decomposition, you should order your nuisance parameters from slowest to
# fastest.
# Cosmological parameters list
data.parameters['omega_b'] = [2.2253, None, None, 0.028, 0.01, 'cosmo']
data.parameters['omega_cdm'] = [0.11919, None, None, 0.0027, 1, 'cosmo']
data.parameters['H0'] = [67.802, None, None, 1.2, 1, 'cosmo']
data.parameters['A_s'] = [2.2177, 0, None, 0.055, 1.e-9, 'cosmo']
data.parameters['n_s'] = [0.96229, 0, None, 0.0074, 1, 'cosmo']
data.parameters['tau_reio'] = [0.09463, 0, None, 0.013, 1, 'cosmo']
# Nuisance parameter list, same call, except the name does not have to be a class name
data.parameters['A_ps_100'] = [145.83, 0, None, 61, 1, 'nuisance']
data.parameters['A_ps_143'] = [49.578, 0, None, 14, 1, 'nuisance']
data.parameters['A_ps_217'] = [121.36, 0, None, 16, 1, 'nuisance']
data.parameters['A_cib_143'] = [4.3922, 0, 20, 5.4, 1, 'nuisance']
data.parameters['A_cib_217'] = [24.869, 0, None, 7.1, 1, 'nuisance']
data.parameters['A_sz'] = [9.7748, 0, 10, 2.3, 1, 'nuisance']
data.parameters['r_ps'] = [0.92873, 0, 1, 0.074, 1, 'nuisance']
data.parameters['r_cib'] = [0.37566, 0, 1, 0.22, 1, 'nuisance']
data.parameters['n_Dl_cib'] = [0.53809, 0, 2, .12, 1, 'nuisance']
data.parameters['cal_100'] = [1.0006, 0, None, 0.00041, 1, 'nuisance']
data.parameters['cal_217'] = [0.99632, 0, None, 0.0014, 1, 'nuisance']
data.parameters['xi_sz_cib'] = [0.20243, 0, 1, 0.34, 1, 'nuisance']
data.parameters['A_ksz'] = [1.5184, 0, 10, 3.4, 1, 'nuisance']
data.parameters['Bm_1_1'] = [1.1028, None, None, 0.59, 1, 'nuisance']
# Derived parameters
data.parameters['z_reio'] = [1, None, None, 0, 1, 'derived']
data.parameters['Omega_Lambda'] = [1, None, None, 0, 1, 'derived']
data.parameters['YHe'] = [1, None, None, 0, 1, 'derived']
data.parameters['ln10^{10}A_s'] = [0, None, None, 0, 1, 'derived']
# Other cosmo parameters (fixed parameters, precision parameters, etc.)
data.cosmo_arguments['sBBN file'] = data.path['cosmo']+'/bbn/sBBN.dat'
data.cosmo_arguments['k_pivot'] = 0.05
# The base model features two massless
# and one massive neutrino with m=0.06eV.
# The settings below ensures that these
# three species contribute equally
# to the radiation density at large
# redshift, with a total of Neff=3.046
data.cosmo_arguments['N_eff'] = 2.03351
data.cosmo_arguments['N_ncdm'] = 1
data.cosmo_arguments['m_ncdm'] = 0.06
data.cosmo_arguments['T_ncdm'] = 0.715985
# This settings is to get the same
# (arbitrary) reionization width as in CAMB
data.cosmo_arguments['reionization_width']=0.5
#------ Mcmc parameters ----
data.N=10
data.write_step=5