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base_actspt.param
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base_actspt.param
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#------Experiments to test (separated with commas)-----
data.experiments=['Planck_highl', 'Planck_lowl', 'lowlike', 'Planck_actspt']
data.over_sampling=[1, 2, 3]
#------ Parameter list -------
# data.parameters[class name] = [mean, min, max, 1-sigma, scale, role]
# - if min max irrelevant, put to -1
# - if fixed, put 1-sigma to 0
# - if scale irrelevant, put to 1, otherwise to the appropriate factor
# - role is either 'cosmo' or 'nuisance'
# Cosmological parameters list
data.parameters['omega_b'] = [2.2253, -1,-1, 0.028, 0.01, 'cosmo']
data.parameters['omega_cdm'] = [0.11919, -1,-1, 0.0027, 1, 'cosmo']
data.parameters['H0'] = [67.802, -1,-1, 1.2, 1, 'cosmo']
data.parameters['A_s'] = [2.2177, 0,-1, 0.055, 1.e-9, 'cosmo']
data.parameters['n_s'] = [0.96229, 0,-1, 0.0074, 1, 'cosmo']
data.parameters['tau_reio'] = [0.09463, 0,-1, 0.013, 1, 'cosmo']
# Nuisance parameter list, same call, except the name does not have to be a class name
# for Planck
data.parameters['A_ps_100'] = [145.83, 0,-1, 61, 1, 'nuisance']
data.parameters['A_ps_143'] = [49.578, 0,-1, 14, 1, 'nuisance']
data.parameters['A_ps_217'] = [121.36, 0,-1, 16, 1, 'nuisance']
data.parameters['A_cib_143'] = [4.3922, 0,20, 5.4, 1, 'nuisance']
data.parameters['A_cib_217'] = [24.869, 0,-1, 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,-1,0.00041, 1, 'nuisance']
data.parameters['cal_217'] = [0.99632, 0,-1, 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, -1,-1, 0.59, 1, 'nuisance']
# for ACT
data.parameters['a_ps_act_148'] = [10.28, 0, 30, 0.60, 1, 'nuisance']
data.parameters['a_ps_act_217'] = [75.59, 0, 200, 4.5, 1, 'nuisance']
data.parameters['r_ps_150x220'] = [0.9077, 0, 1, 0.023, 1, 'nuisance']
data.parameters['a_ge'] = [0.837, 0, 5, 0.20, 1, 'nuisance']
data.parameters['a_gs'] = [0.421, 0, 5, 0.19, 1, 'nuisance']
data.parameters['cal_acte_148'] = [0.9874, 0.8, 1.3, 0.0072, 1, 'nuisance']
data.parameters['cal_acte_217'] = [0.9611, 0.8, 1.3, 0.010, 1, 'nuisance']
data.parameters['cal_acts_148'] = [0.9916, 0.8, 1.3, 0.0073, 1, 'nuisance']
data.parameters['cal_acts_217'] = [1.0029, 0.8, 1.3, 0.013, 1, 'nuisance']
# for SPT
data.parameters['a_ps_spt_95'] = [7.22, 0, 30, 1.5, 1, 'nuisance']
data.parameters['a_ps_spt_150'] = [9.73, 0, 30, 0.52, 1, 'nuisance']
data.parameters['a_ps_spt_220'] = [72.52, 0, 200, 4.5, 1, 'nuisance']
data.parameters['r_ps_spt_95x150']= [0.814, 0, 1, 0.090, 1, 'nuisance']
data.parameters['r_ps_spt_95x220']= [0.564, 0, 1, 0.12, 1, 'nuisance']
data.parameters['cal_spt_95'] = [0.9844, 0.8, 1.3, 0.019, 1, 'nuisance']
data.parameters['cal_spt_150'] = [0.9844, 0.8, 1.3, 0.0096, 1, 'nuisance']
data.parameters['cal_spt_220'] = [1.0179, 0.8, 1.3, 0.023, 1, 'nuisance']
# Derived parameters
data.parameters['z_reio'] = [1,-1,-1, 0, 1, 'derived']
data.parameters['Omega_Lambda'] = [1,-1,-1, 0, 1, 'derived']
data.parameters['YHe'] = [1,-1,-1, 0, 1, 'derived']
data.parameters['ln10^{10}A_s'] = [0,-1,-1, 0, 1, 'derived']
# Other cosmo parameters (fixed parameters, precision parameters, etc.)
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