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base2015.param
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base2015.param
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#------Experiments to test (separated with commas)-----
data.experiments=['Planck_highl','Planck_lowl','Planck_lensing']
#------ 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['100*theta_s'] = [ 1.0418, None, None, 3e-4, 1, 'cosmo']
data.parameters['ln10^{10}A_s'] = [ 3.0753, None, None, 0.0029, 1, 'cosmo']
data.parameters['n_s'] = [0.96229, None, None, 0.0074, 1, 'cosmo']
data.parameters['tau_reio'] = [0.09463, 0.04, None, 0.013, 1, 'cosmo']
# Nuisance parameter list, same call, except the name does not have to be a class name
data.parameters['A_cib_217'] = [ 61, 0, 200, 7, 1,'nuisance']
data.parameters['cib_index'] = [ -1.3, -1.3, -1.3, 0, 1,'nuisance']
data.parameters['xi_sz_cib'] = [ 0.13, 0, 1, 0.3, 1,'nuisance']
data.parameters['A_sz'] = [ 6.86, 0, 10, 1.9, 1,'nuisance']
data.parameters['ps_A_100_100'] = [ 222.9, 0, 400, 30, 1,'nuisance']
data.parameters['ps_A_143_143'] = [ 38, 0, 400, 8, 1,'nuisance']
data.parameters['ps_A_143_217'] = [ 35.2, 0, 400, 10, 1,'nuisance']
data.parameters['ps_A_217_217'] = [ 102.6, 0, 400, 11, 1,'nuisance']
data.parameters['ksz_norm'] = [ 0, 0, 10, 4.2, 1,'nuisance']
data.parameters['gal545_A_100'] = [ 6.75, 0, 50, 1.8, 1,'nuisance']
data.parameters['gal545_A_143'] = [ 9.41, 0, 50, 1.8, 1,'nuisance']
data.parameters['gal545_A_143_217'] = [ 19.28, 0, 100, 4.2, 1,'nuisance']
data.parameters['gal545_A_217'] = [ 81.7, 0, 400, 7.9, 1,'nuisance']
data.parameters['calib_100T'] = [ 998.59, 0, 3000, 0.73, 0.001,'nuisance']
data.parameters['calib_217T'] = [ 995.89, 0, 3000, 1.4, 0.001,'nuisance']
data.parameters['A_planck'] = [100.028, 90, 110, 0.25, 0.01,'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['H0'] = [0, None, None, 0, 1, 'derived']
data.parameters['A_s'] = [0, None, None, 0, 1e-9, 'derived']
data.parameters['sigma8'] = [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 Neff=3.046
# and m/omega = 93.14 eV
data.cosmo_arguments['N_ur'] = 2.0328
data.cosmo_arguments['N_ncdm'] = 1
data.cosmo_arguments['m_ncdm'] = 0.06
data.cosmo_arguments['T_ncdm'] = 0.71611
# These two are required to get sigma8 as a derived parameter
# (class must compute the P(k) until sufficient k)
data.cosmo_arguments['output'] = 'mPk'
data.cosmo_arguments['P_k_max_h/Mpc'] = 1.
#------ Mcmc parameters ----
data.N=10
data.write_step=5