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Added example runcard for contamination studies
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James Moore
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Oct 10, 2023
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n3fit/runcards/examples/simunet_examples/example_contamination.yaml
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############################################################ | ||
description: "Runcard template for the new (more flexible) contaminated fits. This one sets up a fit with contaminated pseudodata when we run the vp-contaminate script. We can then run n3fit as normal." | ||
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############################################################ | ||
dataset_inputs: | ||
- {dataset: NMC, frac: 0.75} | ||
- {dataset: ATLASTTBARTOT7TEV, cfac: [QCD], contamination: 'EFT-LO'} | ||
- {dataset: ATLAS_TOPDIFF_DILEPT_8TEV_TTMNORM, cfac: [QCD], contamination: 'EFT-LO'} | ||
- {dataset: ATLAS_TTBAR_8TEV_ASY, cfac: [QCD], contamination: 'EFT-LO'} | ||
- {dataset: ATLAS_SINGLETOPW_8TEV_TOTAL, use_fixed_predictions: True, contamination: 'EFT-LO'} | ||
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fixed_pdf_fit: False | ||
# load_weights_from_fit: 221103-jmm-no_top_1000_iterated # If this is uncommented, training starts here. | ||
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########################################################### | ||
# The closure test namespace tells us the settings for the | ||
# (possible contaminated) closure test. | ||
############################################################ | ||
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 | ||
contamination_parameters: | ||
- {name: 'OtG', value: 0.01} | ||
- {name: 'Opt', value: 0.02} | ||
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seed: 0 | ||
rngalgo: 0 | ||
############################################################ | ||
datacuts: | ||
t0pdfset: NNPDF40_nlo_as_01180 # PDF set to generate t0 covmat | ||
q2min: 3.49 # Q2 minimum | ||
w2min: 12.5 # W2 minimum | ||
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############################################################ | ||
theory: | ||
theoryid: 200 # database id | ||
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############################################################ | ||
trvlseed: 475038818 | ||
nnseed: 2394641471 | ||
mcseed: 1831662593 | ||
save: "weights.h5" | ||
genrep: true # true = generate MC replicas, false = use real data | ||
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############################################################ | ||
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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: 30000 | ||
positivity: | ||
initial: 184.8 | ||
multiplier: | ||
integrability: | ||
initial: 184.8 | ||
multiplier: | ||
stopping_patience: 0.2 | ||
layer_type: dense | ||
dropout: 0.0 | ||
threshold_chi2: 3.5 | ||
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fitting: | ||
# EVOL(QED) = sng=0,g=1,v=2,v3=3,v8=4,t3=5,t8=6,(pht=7) | ||
# EVOLS(QED)= sng=0,g=1,v=2,v8=4,t3=4,t8=5,ds=6,(pht=7) | ||
# FLVR(QED) = g=0, u=1, ubar=2, d=3, dbar=4, s=5, sbar=6, (pht=7) | ||
fitbasis: EVOL # EVOL (7), EVOLQED (8), etc. | ||
basis: | ||
- {fl: sng, pos: false, trainable: false, mutsize: [15], mutprob: [0.05], smallx: [ | ||
1.093, 1.121], largex: [1.486, 3.287]} | ||
- {fl: g, pos: false, trainable: false, mutsize: [15], mutprob: [0.05], smallx: [ | ||
0.8329, 1.071], largex: [3.084, 6.767]} | ||
- {fl: v, pos: false, trainable: false, mutsize: [15], mutprob: [0.05], smallx: [ | ||
0.5202, 0.7431], largex: [1.556, 3.639]} | ||
- {fl: v3, pos: false, trainable: false, mutsize: [15], mutprob: [0.05], smallx: [ | ||
0.1205, 0.4839], largex: [1.736, 3.622]} | ||
- {fl: v8, pos: false, trainable: false, mutsize: [15], mutprob: [0.05], smallx: [ | ||
0.5864, 0.7987], largex: [1.559, 3.569]} | ||
- {fl: t3, pos: false, trainable: false, mutsize: [15], mutprob: [0.05], smallx: [ | ||
-0.5019, 1.126], largex: [1.754, 3.479]} | ||
- {fl: t8, pos: false, trainable: false, mutsize: [15], mutprob: [0.05], smallx: [ | ||
0.6305, 0.8806], largex: [1.544, 3.481]} | ||
- {fl: t15, pos: false, trainable: false, mutsize: [15], mutprob: [0.05], smallx: [ | ||
1.087, 1.139], largex: [1.48, 3.365]} | ||
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############################################################ | ||
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} | ||
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############################################################ | ||
integrability: | ||
integdatasets: | ||
- {dataset: INTEGXT8, maxlambda: 1e2} | ||
- {dataset: INTEGXT3, maxlambda: 1e2} | ||
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############################################################ | ||
debug: false | ||
maxcores: 4 |