-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathexample_contamination.yaml
203 lines (187 loc) · 8.16 KB
/
example_contamination.yaml
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
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
#
# Configuration file for n3fit
#
description: NNPDF4.0 NLO global, alphas=0.118
############################################################
# frac: training fraction
# ewk: apply ewk k-factors
# sys: systematics treatment (see systypes)
#
NLODatasets: &NLODatasets
- {dataset: NMCPD_dw_ite, frac: 0.75}
- {dataset: NMC, frac: 0.75, cfac: ["Y"]}
- {dataset: SLACP_dwsh, frac: 0.75}
- {dataset: SLACD_dw_ite, frac: 0.75}
- {dataset: BCDMSP_dwsh, frac: 0.75}
- {dataset: BCDMSD_dw_ite, frac: 0.75}
- {dataset: CHORUSNUPb_dw_ite, frac: 0.75}
- {dataset: CHORUSNBPb_dw_ite, frac: 0.75}
- {dataset: NTVNUDMNFe_dw_ite, frac: 0.75, cfac: []}
- {dataset: NTVNBDMNFe_dw_ite, frac: 0.75, cfac: []}
- {dataset: HERACOMBNCEM, frac: 0.75, cfac: ["Y"]}
- {dataset: HERACOMBNCEP460, frac: 0.75, cfac: ["Y"]}
- {dataset: HERACOMBNCEP575, frac: 0.75, cfac: ["Y"]}
- {dataset: HERACOMBNCEP820, frac: 0.75, cfac: ["Y"]}
- {dataset: HERACOMBNCEP920, frac: 0.75, cfac: ["Y"]}
- {dataset: HERACOMBCCEM, frac: 0.75}
- {dataset: HERACOMBCCEP, frac: 0.75}
- {dataset: HERACOMB_SIGMARED_C, frac: 0.75}
- {dataset: HERACOMB_SIGMARED_B, frac: 0.75}
- {dataset: DYE886R_dw_ite, frac: 0.75}
- {dataset: DYE886P, frac: 0.75, cfac: []}
- {dataset: DYE605_dw_ite, frac: 0.75, cfac: []}
- {dataset: DYE906R_dw_ite, frac: 0.75, cfac: [ACC]}
- {dataset: CDFZRAP_NEW, frac: 0.75, cfac: []}
- {dataset: D0ZRAP_40, frac: 0.75, cfac: []}
- {dataset: D0WMASY, frac: 0.75, cfac: []}
- {dataset: ATLASWZRAP36PB, frac: 0.75, cfac: []}
- {dataset: ATLASZHIGHMASS49FB, frac: 0.75, cfac: ["Y"]}
- {dataset: ATLASLOMASSDY11EXT, frac: 0.75, cfac: []}
- {dataset: ATLASWZRAP11CC, frac: 0.75, cfac: []}
- {dataset: ATLASWZRAP11CF, frac: 0.75, cfac: []}
- {dataset: ATLASDY2D8TEV, frac: 0.75, cfac: ["Y"]}
- {dataset: ATLAS_DY_2D_8TEV_LOWMASS, frac: 0.75, cfac: []}
- {dataset: ATLAS_WZ_TOT_13TEV, frac: 0.75, cfac: [NRM]}
- {dataset: ATLAS_WP_JET_8TEV_PT, frac: 0.75, cfac: []}
- {dataset: ATLAS_WM_JET_8TEV_PT, frac: 0.75, cfac: []}
- {dataset: ATLAS_WCHARM_WP_DIFF_7TEV, frac: 0.75, cfac: []}
- {dataset: ATLAS_WCHARM_WM_DIFF_7TEV, frac: 0.75, cfac: []}
- {dataset: ATLASZPT8TEVMDIST, frac: 0.75, cfac: []}
- {dataset: ATLASZPT8TEVYDIST, frac: 0.75, cfac: []}
- {dataset: ATLASTTBARTOT7TEV, frac: 0.75, cfac: []}
- {dataset: ATLASTTBARTOT8TEV, frac: 0.75, cfac: []}
- {dataset: ATLAS_TTBARTOT_13TEV_FULLLUMI, frac: 0.75, cfac: []}
- {dataset: ATLAS_TTB_DIFF_8TEV_LJ_TRAPNORM, frac: 0.75, cfac: []}
- {dataset: ATLAS_TTB_DIFF_8TEV_LJ_TTRAPNORM, frac: 0.75, cfac: []}
- {dataset: ATLAS_TOPDIFF_DILEPT_8TEV_TTRAPNORM, frac: 0.75, cfac: []}
- {dataset: ATLAS_1JET_8TEV_R06_DEC, frac: 0.75, cfac: []}
- {dataset: ATLAS_2JET_7TEV_R06, frac: 0.75, cfac: []}
- {dataset: ATLASPHT15_SF, frac: 0.75, cfac: [EWK]}
- {dataset: ATLAS_SINGLETOP_TCH_R_7TEV, frac: 0.75, cfac: []}
- {dataset: ATLAS_SINGLETOP_TCH_R_13TEV, frac: 0.75, cfac: []}
- {dataset: ATLAS_SINGLETOP_TCH_DIFF_7TEV_T_RAP_NORM, frac: 0.75, cfac: []}
- {dataset: ATLAS_SINGLETOP_TCH_DIFF_7TEV_TBAR_RAP_NORM, frac: 0.75, cfac: []}
- {dataset: ATLAS_SINGLETOP_TCH_DIFF_8TEV_T_RAP_NORM, frac: 0.75, cfac: []}
- {dataset: ATLAS_SINGLETOP_TCH_DIFF_8TEV_TBAR_RAP_NORM, frac: 0.75, cfac: []}
- {dataset: CMSWEASY840PB, frac: 0.75, cfac: []}
- {dataset: CMSWMASY47FB, frac: 0.75, cfac: []}
- {dataset: CMSDY2D11, frac: 0.75, cfac: ["Y"]}
- {dataset: CMSWMU8TEV, frac: 0.75, cfac: []}
- {dataset: CMSZDIFF12, frac: 0.75, cfac: [NRM]}
- {dataset: CMS_2JET_7TEV, frac: 0.75, cfac: []}
- {dataset: CMS_1JET_8TEV, frac: 0.75, cfac: []}
- {dataset: CMSTTBARTOT7TEV, frac: 0.75, cfac: []}
- {dataset: CMSTTBARTOT8TEV, frac: 0.75, cfac: []}
- {dataset: CMSTTBARTOT13TEV, frac: 0.75, cfac: []}
- {dataset: CMSTOPDIFF8TEVTTRAPNORM, frac: 0.75, cfac: []}
- {dataset: CMSTTBARTOT5TEV, frac: 0.75, cfac: []}
- {dataset: CMS_TTBAR_2D_DIFF_MTT_TRAP_NORM, frac: 0.75, cfac: []}
- {dataset: CMS_TTB_DIFF_13TEV_2016_2L_TRAP, frac: 0.75, cfac: []}
- {dataset: CMS_TTB_DIFF_13TEV_2016_LJ_TRAP, frac: 0.75, cfac: []}
- {dataset: CMS_SINGLETOP_TCH_TOT_7TEV, frac: 0.75, cfac: []}
- {dataset: CMS_SINGLETOP_TCH_R_8TEV, frac: 0.75, cfac: []}
- {dataset: CMS_SINGLETOP_TCH_R_13TEV, frac: 0.75, cfac: []}
- {dataset: CMSWCHARMTOT, frac: 0.75}
- {dataset: CMSWCHARMRAT, frac: 0.75}
- {dataset: CMS_WCHARM_DIFF_UNNORM_13TEV, frac: 0.75}
- {dataset: LHCBZ940PB, frac: 0.75, cfac: []}
- {dataset: LHCBZEE2FB_40, frac: 0.75, cfac: []}
- {dataset: LHCBWZMU7TEV, frac: 0.75, cfac: [NRM]}
- {dataset: LHCBWZMU8TEV, frac: 0.75, cfac: [NRM]}
- {dataset: LHCB_Z_13TEV_DIMUON, frac: 0.75, cfac: []}
- {dataset: LHCB_Z_13TEV_DIELECTRON, frac: 0.75, cfac: []}
- {dataset: CMSDY1D12, frac: 0.75, cfac: ["Y"]}
- {dataset: CMS_HMDY_13TEV, frac: 0.75, cfac: ["Y"]}
dataset_inputs: *NLODatasets
write_cfactors_data:
- {name: "Y", new_value: 0.1, default_value: -0.001, quad_on: False}
output_name: Y0p1
############################################################
datacuts:
t0pdfset: 210712-theory-001 # PDF set to generate t0 covmat
q2min: 3.49 # Q2 minimum
w2min: 12.5 # W2 minimum
use_cuts: internal
############################################################
theory:
theoryid: 208 # database id
############################################################
trvlseed: 376191634
nnseed: 2080989803
mcseed: 75955222
save: false
genrep: true # true = generate MC replicas, false = use real data
parameters: # This defines the parameter dictionary that is passed to the Model Trainer
nodes_per_layer: [25, 20, 8]
activation_per_layer: [tanh, tanh, linear]
initializer: glorot_normal
optimizer:
clipnorm: 6.073e-6
learning_rate: 2.621e-3
optimizer_name: Nadam
epochs: 17000
positivity:
initial: 184.8
multiplier:
integrability:
initial: 10
multiplier:
stopping_patience: 0.1
layer_type: dense
dropout: 0.0
threshold_chi2: 3.5
fitting:
fitbasis: EVOL # EVOL (7), EVOLQED (8), etc.
basis:
- {fl: sng, trainable: false, smallx: [1.121, 1.154], largex: [1.498, 3.138]}
- {fl: g, trainable: false, smallx: [0.9224, 1.149], largex: [3.266, 6.214]}
- {fl: v, trainable: false, smallx: [0.5279, 0.8017], largex: [1.6, 3.588]}
- {fl: v3, trainable: false, smallx: [0.2011, 0.4374], largex: [1.761, 3.427]}
- {fl: v8, trainable: false, smallx: [0.5775, 0.8357], largex: [1.589, 3.378]}
- {fl: t3, trainable: false, smallx: [-0.484, 1.0], largex: [1.763, 3.397]}
- {fl: t8, trainable: false, smallx: [0.6714, 0.9197], largex: [1.572, 3.496]}
- {fl: t15, trainable: false, smallx: [1.073, 1.164], largex: [1.503, 3.636]}
############################################################
positivity:
posdatasets:
- {dataset: POSF2U, maxlambda: 1e6} # Positivity Lagrange Multiplier
- {dataset: POSF2DW, maxlambda: 1e6}
- {dataset: POSF2S, maxlambda: 1e6}
- {dataset: POSFLL, maxlambda: 1e6}
- {dataset: POSDYU, maxlambda: 1e10}
- {dataset: POSDYD, maxlambda: 1e10}
- {dataset: POSDYS, maxlambda: 1e10}
- {dataset: POSF2C, maxlambda: 1e6}
- {dataset: POSXUQ, maxlambda: 1e6} # Positivity of MSbar PDFs
- {dataset: POSXUB, maxlambda: 1e6}
- {dataset: POSXDQ, maxlambda: 1e6}
- {dataset: POSXDB, maxlambda: 1e6}
- {dataset: POSXSQ, maxlambda: 1e6}
- {dataset: POSXSB, maxlambda: 1e6}
- {dataset: POSXGL, maxlambda: 1e6}
############################################################
integrability:
integdatasets:
- {dataset: INTEGXT8, maxlambda: 1e2}
- {dataset: INTEGXT3, maxlambda: 1e2}
closuretest:
filterseed: 0 # Random seed to be used in filtering data partitions
fakedata: true # true = to use FAKEPDF to generate pseudo-data
fakepdf: NNPDF40_nlo_as_01180 # Theory input for pseudo-data
errorsize: 1.0 # uncertainties rescaling
fakenoise: true # true = to add random fluctuations to pseudo-data
rancutprob: 1.0 # Fraction of data to be included in the fit
rancutmethod: 0 # Method to select rancutprob data fraction
rancuttrnval: false # 0(1) to output training(valiation) chi2 in report
printpdf4gen: false # To print info on PDFs during minimization
seed: 0
rngalgo: 0
############################################################
debug: false
maxcores: 4
actions_:
- write_cfactor_files
- write_runcard
- launch_vpsetupfit
- launch_vprebuilddata
- write_sm_runcard