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Merge branch 'master' into new_ht_thcovmat
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.github/workflows/fitbot.yml

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POSTFIT_NREP: 15 # requested minimum replicas for postfit
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# IMPORTANT
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# WHEN CHANGING THE REFERENCE SET, THE NEW REFERENCE MUST BE MANUALLY UPLOADED TO THE SERVER
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REFERENCE_SET: NNNBOT-1a81255f3-2026-01-27 # reference set for exact results
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REFERENCE_SET: NNBOT-351efa9ef-2026-03-11 # reference set for exact results
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STABLE_REFERENCE_SET: NNBOT-99108504e-2025-11-22 # reference set for last tag
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PYTHONHASHSEED: "0"
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.github/workflows/redo_regressions.yml

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fetch-depth: 0
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ref: ${{ github.event.pull_request.head.ref }}
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- uses: ./.github/actions/install_conda_pip
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with:
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python-version: "[qed,tests]"
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- name: Regenerate regressions
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shell: bash -l {0}
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run: |

extra_tests/regression_checks.py

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"polarized_evol": 34,
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"t0theoryid": 100,
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"no_t0_sampling": 430,
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"hyperopt_sampling": 4,
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}
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extra_tests/regression_fits/central_16.json

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4.779973403498672e-05,
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0.46639937162399386,
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0.006792142638006371
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]
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],
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"hyperparameters": "from runcard"
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}

extra_tests/regression_fits/feature_scaling_81.json

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2.446863373944952e-05,
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0.05534580023959301,
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0.010789581050630881
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]
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],
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"hyperparameters": "from runcard"
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}

extra_tests/regression_fits/flavour_29.json

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0.08181699179112917,
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0.10192120075225847,
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0.5177502781152727
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]
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],
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"hyperparameters": "from runcard"
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}
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#
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# Configuration file for n3fit regression tests
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# This runcard includes two DIS datasets, one Hadronic dataset
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# and two positivity datasets
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#
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############################################################
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description: n3fit regression test for hyperotp sampling in NNPDF4.1
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############################################################
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# frac: training fraction
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# ewk: apply ewk k-factors
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# sys: systematics treatment (see systypes)
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dataset_inputs:
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- {dataset: NMC_NC_NOTFIXED_P_EM-SIGMARED, variant: legacy}
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- {dataset: SLAC_NC_NOTFIXED_P_EM-F2, variant: legacy_dw}
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- {dataset: CMS_Z0J_8TEV_PT-Y}
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- {dataset: ATLAS_TTBAR_8TEV_TOT_X-SEC}
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############################################################
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datacuts:
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t0pdfset: NNPDF40_nnlo_as_01180 # PDF set to generate t0 covmat
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q2min : 3.49 # Q2 minimum
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w2min : 12.5 # W2 minimum
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############################################################
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theory:
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theoryid: 41_000_000 # database id
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############################################################
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genrep: False # on = generate MC replicas, False = use real data
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trvlseed: 3
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nnseed: 2
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mcseed: 1
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trial_specs:
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hyperscan: 260204-jcm-hyperopt
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thermalization: 400
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number_of_trials: 10
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parameters: # This defines the parameter dictionary that is passed to the Model Trainer
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positivity:
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multiplier: 1.05
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initial: 1.5
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threshold_chi2: 10.0
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feature_scaling_points: 5
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fitting:
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fitbasis: CCBAR_ASYMM # EVOL (7), EVOLQED (8), etc.
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savepseudodata: False
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basis:
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- {fl: sng, trainable: false, smallx: [1.058, 1.155]}
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- {fl: g, trainable: false, smallx: [0.9017, 1.084]}
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- {fl: v, trainable: false, smallx: [0.481, 0.6499]}
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- {fl: v3, trainable: false, smallx: [0.08225, 0.502]}
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- {fl: v8, trainable: false, smallx: [0.5823, 0.7928]}
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- {fl: t3, trainable: false, smallx: [-0.3987, 0.9689]}
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- {fl: t8, trainable: false, smallx: [0.6077, 0.9459]}
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- {fl: t15, trainable: false, smallx: [1.023, 1.147]}
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- {fl: v15, trainable: false, smallx: [0.5005, 0.7189]}
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############################################################
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positivity:
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posdatasets:
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- {dataset: NNPDF_POS_2P24GEV_F2U, maxlambda: 1e6}
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- {dataset: NNPDF_POS_2P24GEV_DYS, maxlambda: 1e5}
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integrability:
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integdatasets:
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- {dataset: NNPDF_INTEG_3GEV_XT8, maxlambda: 1e2}
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############################################################
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debug: true
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double_precision: false

extra_tests/regression_fits/hyperopt_sampling_4.exportgrid

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{
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"preprocessing": [
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{
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"fl": "sng",
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"smallx": 1.1070080995559692,
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"largex": 0.0,
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"trainable": false
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},
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{
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"fl": "g",
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"smallx": 1.0574088096618652,
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"largex": 0.0,
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"trainable": false
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},
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{
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"fl": "v",
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"smallx": 0.5285826325416565,
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"largex": 0.0,
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"trainable": false
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},
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{
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"fl": "v3",
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"smallx": 0.46124693751335144,
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"largex": 0.0,
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"trainable": false
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},
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{
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"fl": "v8",
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"smallx": 0.7556604743003845,
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"largex": 0.0,
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"trainable": false
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},
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{
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"fl": "t3",
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"smallx": 0.7078039646148682,
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"largex": 0.0,
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"trainable": false
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},
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{
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"fl": "t8",
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"smallx": 0.6369834542274475,
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"largex": 0.0,
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"trainable": false
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},
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{
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"fl": "t15",
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"smallx": 1.1202620267868042,
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"largex": 0.0,
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"trainable": false
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},
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{
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"fl": "v15",
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"smallx": 0.5717487931251526,
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"largex": 0.0,
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"trainable": false
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}
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],
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"stop_epoch": 15700,
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"best_epoch": 11809,
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"erf_tr": 1.5522006750106812,
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"erf_vl": 0.8228918313980103,
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"chi2": 1.3685028535082824,
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"pos_state": "POS_PASS",
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"arc_lengths": [
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2.523857734518874,
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1.101830505438013,
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5.354887791586663,
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1.002221739520188,
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1.0815427015576187
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],
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"integrability": [
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0.02235139708500272,
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0.036644561681897336,
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0.0004369386006155551,
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0.02273471909575253,
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0.04385659284889787
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],
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"hyperparameters": {
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"optimizer": "Adam",
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"learning_rate": 0.007521481879943997,
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"clipnorm": 2.7161295780819e-05,
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"epochs": 15756,
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"stopping_patience": 0.4200000000000001,
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"initial": 15.789743720813016,
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"nodes_per_layer": [
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30,
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40,
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15,
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52,
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9
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],
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"number_of_layers": 4,
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"activation": "sigmoid",
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"layer_type": "dense",
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"initializer": "glorot_normal",
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"dropout": 0.0
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}
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}

extra_tests/regression_fits/no_csr_613.json

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1.8668532221099987e-05,
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0.0024789599992791622,
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0.018115404061971763
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]
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],
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"hyperparameters": "from runcard"
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}

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