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Basic_runcard.yml
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Basic_runcard.yml
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#
# Configuration file for n3fit
#
############################################################
description: Basic runcard
############################################################
# frac: training fraction
# ewk: apply ewk k-factors
# sys: systematics treatment (see systypes)
dataset_inputs:
- { dataset: SLACP_dwsh, frac: 0.5}
- { dataset: NMCPD_dw, frac: 0.5 }
- { dataset: ATLASZPT8TEVMDIST, frac: 0.75, sys: 10, cfac: [QCD] }
############################################################
datacuts:
t0pdfset : NNPDF40_nnlo_as_01180 # PDF set to generate t0 covmat
q2min : 3.49 # Q2 minimum
w2min : 12.5 # W2 minimum
combocuts : NNPDF31 # NNPDF3.0 final kin. cuts
jetptcut_tev : 0 # jet pt cut for tevatron
jetptcut_lhc : 0 # jet pt cut for lhc
wptcut_lhc : 30.0 # Minimum pT for W pT diff distributions
jetycut_tev : 1e30 # jet rap. cut for tevatron
jetycut_lhc : 1e30 # jet rap. cut for lhc
dymasscut_min: 0 # dy inv.mass. min cut
dymasscut_max: 1e30 # dy inv.mass. max cut
jetcfactcut : 1e30 # jet cfact. cut
############################################################
theory:
theoryid: 200 # database id
sampling:
use_t0: false
separate_multiplicative: true
parameters: # This defines the parameter dictionary that is passed to the Model Trainer
nodes_per_layer: [15, 10, 8]
activation_per_layer: ['sigmoid', 'sigmoid', 'linear']
initializer: 'glorot_normal'
optimizer:
optimizer_name: 'RMSprop'
learning_rate: 0.01
clipnorm: 1.0
epochs: 900
positivity:
multiplier: 1.05 # When any of the multiplier and/or the initial is not set
initial: # the maxlambda will be used instead to compute these values per dataset
threshold: 1e-5
stopping_patience: 0.30 # percentage of the number of epochs
layer_type: 'dense'
dropout: 0.0
threshold_chi2: 5.0
############################################################
trvlseed: 1
nnseed: 2
mcseed: 3
genrep: True # true = generate MC replicas, false = use real data
fitting:
# NN23(QED) = sng=0,g=1,v=2,t3=3,ds=4,sp=5,sm=6,(pht=7)
# 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: NN31IC # EVOL (7), EVOLQED (8), etc.
basis:
# remeber to change the name of PDF accordingly with fitbasis
- { fl: sng, smallx: [1.05,1.19], largex: [1.47,2.70], trainable: False }
- { fl: g, smallx: [0.94,1.25], largex: [0.11,5.87], trainable: False }
- { fl: v, smallx: [0.54,0.75], largex: [1.15,2.76], trainable: False }
- { fl: v3, smallx: [0.21,0.57], largex: [1.35,3.08] }
- { fl: v8, smallx: [0.52,0.76], largex: [0.77,3.56], trainable: True }
- { fl: t3, smallx: [-0.37,1.52], largex: [1.74,3.39] }
- { fl: t8, smallx: [0.56,1.29], largex: [1.45,3.03] }
- { fl: cp, smallx: [0.12,1.19], largex: [1.83,6.70] }
############################################################
positivity:
posdatasets:
- { dataset: POSF2U, maxlambda: 1e6 } # Positivity Lagrange Multiplier
- { dataset: POSFLL, maxlambda: 1e4 }
############################################################
integrability:
integdatasets:
- {dataset: INTEGXT3, maxlambda: 1e2}
############################################################
debug: True
maxcores: 8
tensorboard:
weight_freq: 100
profiling: False
save: 'weights.h5'
# load: '/path/to/weights.h5/file'