forked from baudren/montepython_public
-
Notifications
You must be signed in to change notification settings - Fork 92
Expand file tree
/
Copy pathbase2018TTTEEE.param
More file actions
128 lines (111 loc) · 8.03 KB
/
Copy pathbase2018TTTEEE.param
File metadata and controls
128 lines (111 loc) · 8.03 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
#------Experiments to test (separated with commas)-----
data.experiments=['Planck_highl_TTTEEE', 'Planck_lowl_EE', 'Planck_lowl_TT']
#------ 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, 5]
#------ 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'] = [ 2.2377, None, None, 0.015, 0.01, 'cosmo']
data.parameters['omega_cdm'] = [ 0.12010, None, None, 0.0013, 1, 'cosmo']
data.parameters['100*theta_s'] = [ 1.04110, None, None, 0.00030, 1, 'cosmo']
data.parameters['ln10^{10}A_s'] = [ 3.0447, None, None, 0.015, 1, 'cosmo']
data.parameters['n_s'] = [ 0.9659, None, None, 0.0042, 1, 'cosmo']
data.parameters['tau_reio'] = [ 0.0543, 0.004, None, 0.008, 1, 'cosmo']
# Nuisance parameter list, same call, except the name does not have to be a class name
data.parameters['A_cib_217'] = [ 47.2, 0, 200, 6.2593, 1, 'nuisance']
data.parameters['cib_index'] = [ -1.3, -1.3, -1.3, 0, 1, 'nuisance']
data.parameters['xi_sz_cib'] = [ 0.42, 0, 1, 0.33, 1, 'nuisance']
data.parameters['A_sz'] = [ 7.23, 0, 10, 1.4689, 1, 'nuisance']
data.parameters['ps_A_100_100'] = [ 251.0, 0, 400, 29.438, 1, 'nuisance']
data.parameters['ps_A_143_143'] = [ 47.4, 0, 400, 9.9484, 1, 'nuisance']
data.parameters['ps_A_143_217'] = [ 47.3, 0, 400, 11.356, 1, 'nuisance']
data.parameters['ps_A_217_217'] = [ 119.8, 0, 400, 10.256, 1, 'nuisance']
data.parameters['ksz_norm'] = [ 0.01, 0, 10, 2.7468, 1, 'nuisance']
data.parameters['gal545_A_100'] = [ 8.86, 0, 50, 1.8928, 1, 'nuisance']
data.parameters['gal545_A_143'] = [ 11.10, 0, 50, 1.8663, 1, 'nuisance']
data.parameters['gal545_A_143_217'] = [ 19.8, 0, 100, 3.8796, 1, 'nuisance']
data.parameters['gal545_A_217'] = [ 95.1, 0, 400, 6.9759, 1, 'nuisance']
data.parameters['galf_EE_A_100'] = [ 0.055, 0.055, 0.055, 0, 1, 'nuisance']
data.parameters['galf_EE_A_100_143'] = [ 0.040, 0.040, 0.040, 0, 1, 'nuisance']
data.parameters['galf_EE_A_100_217'] = [ 0.094, 0.094, 0.094, 0, 1, 'nuisance']
data.parameters['galf_EE_A_143'] = [ 0.086, 0.086, 0.086, 0, 1, 'nuisance']
data.parameters['galf_EE_A_143_217'] = [ 0.21, 0.21, 0.21, 0, 1, 'nuisance']
data.parameters['galf_EE_A_217'] = [ 0.70, 0.70, 0.70, 0, 1, 'nuisance']
data.parameters['galf_EE_index'] = [ -2.4, -2.4, -2.4, 0, 1, 'nuisance']
data.parameters['galf_TE_A_100'] = [ 0.114, 0, 10, 0.038762, 1, 'nuisance']
data.parameters['galf_TE_A_100_143'] = [ 0.134, 0, 10, 0.030096, 1, 'nuisance']
data.parameters['galf_TE_A_100_217'] = [ 0.482, 0, 10, 0.086185, 1, 'nuisance']
data.parameters['galf_TE_A_143'] = [ 0.224, 0, 10, 0.055126, 1, 'nuisance']
data.parameters['galf_TE_A_143_217'] = [ 0.664, 0, 10, 0.082349, 1, 'nuisance']
data.parameters['galf_TE_A_217'] = [ 2.08, 0, 10, 0.27175, 1, 'nuisance']
data.parameters['galf_TE_index'] = [ -2.4, -2.4, -2.4, 0, 1, 'nuisance']
data.parameters['calib_100T'] = [ 999.69, 0, 3000, 0.61251, 0.001, 'nuisance']
data.parameters['calib_217T'] = [ 998.16, 0, 3000, 0.63584, 0.001, 'nuisance']
data.parameters['calib_100P'] = [ 1.021, 1.021, 1.021, 0, 1, 'nuisance']
data.parameters['calib_143P'] = [ 0.966, 0.966, 0.966, 0, 1, 'nuisance']
data.parameters['calib_217P'] = [ 1.040, 1.040, 1.040, 0, 1, 'nuisance']
data.parameters['A_cnoise_e2e_100_100_EE'] = [ 1, 1, 1, 0, 1, 'nuisance']
data.parameters['A_cnoise_e2e_143_143_EE'] = [ 1, 1, 1, 0, 1, 'nuisance']
data.parameters['A_cnoise_e2e_217_217_EE'] = [ 1, 1, 1, 0, 1, 'nuisance']
data.parameters['A_sbpx_100_100_TT'] = [ 1, 1, 1, 0, 1, 'nuisance']
data.parameters['A_sbpx_143_143_TT'] = [ 1, 1, 1, 0, 1, 'nuisance']
data.parameters['A_sbpx_143_217_TT'] = [ 1, 1, 1, 0, 1, 'nuisance']
data.parameters['A_sbpx_217_217_TT'] = [ 1, 1, 1, 0, 1, 'nuisance']
data.parameters['A_sbpx_100_100_EE'] = [ 1, 1, 1, 0, 1, 'nuisance']
data.parameters['A_sbpx_100_143_EE'] = [ 1, 1, 1, 0, 1, 'nuisance']
data.parameters['A_sbpx_100_217_EE'] = [ 1, 1, 1, 0, 1, 'nuisance']
data.parameters['A_sbpx_143_143_EE'] = [ 1, 1, 1, 0, 1, 'nuisance']
data.parameters['A_sbpx_143_217_EE'] = [ 1, 1, 1, 0, 1, 'nuisance']
data.parameters['A_sbpx_217_217_EE'] = [ 1, 1, 1, 0, 1, 'nuisance']
data.parameters['A_planck'] = [ 1.00061, 0.9, 1.1, 0.0025, 1, 'nuisance']
data.parameters['A_pol'] = [ 1, 1, 1, 0, 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['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']+'/external/bbn/sBBN.dat'
# BBN file path is automatically set to match CLASS version if 'sBBN file' is requested
# You can force the code to use the exact BBN file passed above with flag
#data.custom_bbn_file = True
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