From 849888ff94371dda69eb0ea05aa90ab12478da51 Mon Sep 17 00:00:00 2001 From: James Moore Date: Tue, 10 Oct 2023 16:40:36 +0100 Subject: [PATCH] Added example runcard for contamination studies --- .../example_contamination.yaml | 125 ++++++++++++++++++ 1 file changed, 125 insertions(+) create mode 100644 n3fit/runcards/examples/simunet_examples/example_contamination.yaml diff --git a/n3fit/runcards/examples/simunet_examples/example_contamination.yaml b/n3fit/runcards/examples/simunet_examples/example_contamination.yaml new file mode 100644 index 000000000..fb342f2b8 --- /dev/null +++ b/n3fit/runcards/examples/simunet_examples/example_contamination.yaml @@ -0,0 +1,125 @@ +############################################################ +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." + +############################################################ +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'} + +fixed_pdf_fit: False +# load_weights_from_fit: 221103-jmm-no_top_1000_iterated # If this is uncommented, training starts here. + +########################################################### +# 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} + +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 + +############################################################ +theory: + theoryid: 200 # database id + +############################################################ +trvlseed: 475038818 +nnseed: 2394641471 +mcseed: 1831662593 +save: "weights.h5" +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: 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 + +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]} + +############################################################ +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} + +############################################################ +debug: false +maxcores: 4