-
Notifications
You must be signed in to change notification settings - Fork 116
/
base2015TTTEEE.param
171 lines (155 loc) · 11.5 KB
/
base2015TTTEEE.param
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
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
#------Experiments to test (separated with commas)-----
data.experiments=['Planck_highl_TTTEEE','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['galf_EE_A_100'] = [ 0.2082, 0, 10, 0.011, 1,'nuisance']
data.parameters['galf_EE_A_100_143'] = [0.05227, 0, 10,0.0037, 1,'nuisance']
data.parameters['galf_EE_A_100_217'] = [ 0.1471, 0, 10, 0.015, 1,'nuisance']
data.parameters['galf_EE_A_143'] = [ 0.0979, 0, 10,0.0055, 1,'nuisance']
data.parameters['galf_EE_A_143_217'] = [ 0.2464, 0, 10, 0.020, 1,'nuisance']
data.parameters['galf_EE_A_217'] = [ 0.667, 0, 10, 0.059, 1,'nuisance']
data.parameters['galf_EE_index'] = [ -2.4, -2.4, -2.4, 0, 1,'nuisance']
data.parameters['galf_TE_A_100'] = [ 0.184, 0, 10, 0.076, 1,'nuisance']
data.parameters['galf_TE_A_100_143'] = [ 0.2054, 0, 10, 0.035, 1,'nuisance']
data.parameters['galf_TE_A_100_217'] = [ 0.346, 0, 10, 0.19, 1,'nuisance']
data.parameters['galf_TE_A_143'] = [ 0.1892, 0, 10, 0.037, 1,'nuisance']
data.parameters['galf_TE_A_143_217'] = [ 0.375, 0, 10, 0.071, 1,'nuisance']
data.parameters['galf_TE_A_217'] = [ 1.409, 0, 10, 0.19, 1,'nuisance']
data.parameters['galf_TE_index'] = [ -2.4, -2.4, -2.4, 0, 1,'nuisance']
data.parameters['bleak_epsilon_0_0T_0E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_1_0T_0E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_2_0T_0E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_3_0T_0E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_4_0T_0E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_0_0T_1E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_1_0T_1E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_2_0T_1E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_3_0T_1E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_4_0T_1E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_0_0T_2E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_1_0T_2E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_2_0T_2E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_3_0T_2E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_4_0T_2E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_0_1T_1E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_1_1T_1E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_2_1T_1E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_3_1T_1E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_4_1T_1E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_0_1T_2E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_1_1T_2E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_2_1T_2E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_3_1T_2E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_4_1T_2E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_0_2T_2E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_1_2T_2E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_2_2T_2E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_3_2T_2E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_4_2T_2E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_0_0E_0E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_1_0E_0E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_2_0E_0E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_3_0E_0E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_4_0E_0E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_0_0E_1E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_1_0E_1E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_2_0E_1E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_3_0E_1E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_4_0E_1E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_0_0E_2E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_1_0E_2E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_2_0E_2E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_3_0E_2E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_4_0E_2E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_0_1E_1E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_1_1E_1E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_2_1E_1E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_3_1E_1E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_4_1E_1E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_0_1E_2E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_1_1E_2E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_2_1E_2E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_3_1E_2E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_4_1E_2E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_0_2E_2E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_1_2E_2E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_2_2E_2E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_3_2E_2E'] = [ 0, 0, 0, 0, 1,'nuisance']
data.parameters['bleak_epsilon_4_2E_2E'] = [ 0, 0, 0, 0, 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['calib_100P'] = [ 1, 1, 1, 0, 1,'nuisance']
data.parameters['calib_143P'] = [ 1, 1, 1, 0, 1,'nuisance']
data.parameters['calib_217P'] = [ 1, 1, 1, 0, 1,'nuisance']
data.parameters['A_planck'] = [100.028, 90, 110, 0.25, 0.01,'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']+'/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