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Merge pull request #2394 from NNPDF/pos_Q2_10000_dataset
PDF positivity dataset at Q^2 = 10000 GeV^2
2 parents a7d7577 + 47040a0 commit d876f04

13 files changed

Lines changed: 1815 additions & 74 deletions

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‎n3fit/runcards/example-nnpdf41.yml‎

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@@ -2,7 +2,7 @@
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# Configuration file for n3fit
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#
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######################################################################################
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description: Starting runcard for the NNPDF4.1 series of fits. Work In Progress
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description: Runcard for the NNPDF4.1 series of fits; WIP
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######################################################################################
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dataset_inputs:
@@ -28,7 +28,6 @@ dataset_inputs:
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- {dataset: DYE866_Z0_800GEV_DW_RATIO_PDXSECRATIO, frac: 0.75}
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- {dataset: DYE866_Z0_800GEV_PXSEC, frac: 0.75}
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- {dataset: DYE605_Z0_38P8GEV_DW_PXSEC, frac: 0.75}
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# - {dataset: DYE906_Z0_120GEV_DW_PDXSECRATIO, frac: 0.75}
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- {dataset: CDF_Z0_1P96TEV_ZRAP, frac: 0.75}
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- {dataset: D0_Z0_1P96TEV_ZRAP, frac: 0.75}
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- {dataset: D0_WPWM_1P96TEV_ASY, frac: 0.75}
@@ -40,7 +39,6 @@ dataset_inputs:
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- {dataset: ATLAS_Z0_7TEV_46FB_CC-Y, frac: 0.75}
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- {dataset: ATLAS_Z0_7TEV_46FB_CF-Y, frac: 0.75}
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- {dataset: ATLAS_Z0_8TEV_HIMASS_M-Y, frac: 0.75}
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# - {dataset: ATLAS_Z0_8TEV_LOWMASS_M-Y, frac: 0.75, variant: legacy}
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- {dataset: ATLAS_Z0_13TEV_TOT, frac: 0.75, cfac: [NRM]}
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- {dataset: ATLAS_WPWM_13TEV_TOT, frac: 0.75, cfac: [NRM]}
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- {dataset: ATLAS_WJ_8TEV_WP-PT, frac: 0.75}
@@ -89,18 +87,22 @@ dataset_inputs:
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- {dataset: LHCB_Z0_13TEV_DIMUON-Y, frac: 0.75}
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- {dataset: LHCB_Z0_13TEV_DIELECTRON-Y, frac: 0.75}
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################################################################################
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datacuts:
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t0pdfset: 250917-jcm-001
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t0pdfset: 251105-jcm-nnpdf41-mhou
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q2min: 3.49
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w2min: 12.5
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theory:
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theoryid: 41_000_000
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theorycovmatconfig:
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point_prescriptions: [7 point]
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pdf: 251105-jcm-nnpdf41-mhou
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use_thcovmat_in_fitting: true
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use_thcovmat_in_sampling: true
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trvlseed: 1953065998
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nnseed: 1589400026
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mcseed: 2135943670
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trvlseed: 422789893
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nnseed: 1065290715
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mcseed: 819764265
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genrep: true
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parameters: # This defines the parameter dictionary that is passed to the Model Trainer
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nodes_per_layer: [70, 50, 25, 20, 9]
@@ -121,64 +123,34 @@ parameters: # This defines the parameter dictionary that is passed to the Model
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layer_type: dense
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dropout: 0.0
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threshold_chi2: 3.5
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interpolation_points: 5
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interpolation_points: 5
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fitting:
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fitbasis: CCBAR_ASYMM # EVOL (7), EVOLQED (8), etc.
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savepseudodata: true
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basis:
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- {fl: sng, trainable: false, smallx: [1.103, 1.12], largex: [1.461, 3.778]}
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- {fl: g, trainable: false, smallx: [0.8737, 1.088], largex: [2.204, 4.431]}
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- {fl: v, trainable: false, smallx: [0.5051, 0.6847], largex: [1.526, 2.557]}
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- {fl: v3, trainable: false, smallx: [0.1932, 0.4309], largex: [1.73, 2.559]}
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- {fl: v8, trainable: false, smallx: [0.5645, 0.7228], largex: [1.566, 2.662]}
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- {fl: t3, trainable: false, smallx: [-0.4244, 1.0], largex: [1.75, 2.939]}
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- {fl: t8, trainable: false, smallx: [0.6677, 0.9282], largex: [1.55, 3.504]}
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- {fl: t15, trainable: false, smallx: [1.087, 1.136], largex: [1.503, 3.379]}
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- {fl: v15, trainable: false, smallx: [0.4713, 0.7641], largex: [1.464, 3.851]}
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- {fl : sng, trainable : false, smallx : [1.095, 1.146], largex : [0., 0.]}
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- {fl : g, trainable : false, smallx : [0.7978, 1.087], largex : [0., 0.]}
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- {fl : v, trainable : false, smallx : [0.4775, 0.6577], largex : [0., 0.]}
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- {fl : v3, trainable : false, smallx : [0.1068, 0.493], largex : [0., 0.]}
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- {fl : v8, trainable : false, smallx : [0.5914, 0.7776], largex : [0., 0.]}
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- {fl : t3, trainable : false, smallx : [-0.3737, 1.0], largex : [0., 0.]}
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- {fl : t8, trainable : false, smallx : [0.5771, 0.9486], largex : [0., 0.]}
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- {fl : t15, trainable : false, smallx : [1.062, 1.153], largex : [0., 0.]}
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- {fl : v15, trainable : false, smallx : [0.4515, 0.7648], largex : [0., 0.]}
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################################################################################
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positivity:
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posdatasets:
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# Positivity Lagrange Multiplier
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- {dataset: NNPDF_POS_2P24GEV_F2U, maxlambda: 1e6}
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- {dataset: NNPDF_POS_2P24GEV_F2D, maxlambda: 1e6}
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- {dataset: NNPDF_POS_2P24GEV_F2S, maxlambda: 1e6}
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- {dataset: NNPDF_POS_2P24GEV_FLL, maxlambda: 1e6}
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- {dataset: NNPDF_POS_2P24GEV_DYU, maxlambda: 1e10}
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- {dataset: NNPDF_POS_2P24GEV_DYD, maxlambda: 1e10}
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- {dataset: NNPDF_POS_2P24GEV_DYS, maxlambda: 1e10}
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- {dataset: NNPDF_POS_2P24GEV_F2C-CCE, maxlambda: 1e6}
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- {dataset: NNPDF_POS_2P24GEV_F2C-CCP, maxlambda: 1e6}
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# Positivity of MSbar PDFs
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- {dataset: NNPDF_POS_2P24GEV_XUQ, maxlambda: 1e6}
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- {dataset: NNPDF_POS_2P24GEV_XUB, maxlambda: 1e6}
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- {dataset: NNPDF_POS_2P24GEV_XDQ, maxlambda: 1e6}
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- {dataset: NNPDF_POS_2P24GEV_XDB, maxlambda: 1e6}
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- {dataset: NNPDF_POS_2P24GEV_XSQ, maxlambda: 1e6}
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- {dataset: NNPDF_POS_2P24GEV_XSB, maxlambda: 1e6}
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- {dataset: NNPDF_POS_2P24GEV_XGL, maxlambda: 1e6}
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added_filter_rules:
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- dataset: NNPDF_POS_2P24GEV_FLL
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rule: x > 5.0e-7
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- dataset: NNPDF_POS_2P24GEV_F2C-CCE
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rule: x < 0.74
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- dataset: NNPDF_POS_2P24GEV_F2C-CCP
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rule: x < 0.74
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- dataset: NNPDF_POS_2P24GEV_XGL
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rule: x > 0.1
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- dataset: NNPDF_POS_2P24GEV_XUQ
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rule: x > 0.1
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- dataset: NNPDF_POS_2P24GEV_XUB
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rule: x > 0.1
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- dataset: NNPDF_POS_2P24GEV_XDQ
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rule: x > 0.1
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- dataset: NNPDF_POS_2P24GEV_XDB
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rule: x > 0.1
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- dataset: NNPDF_POS_2P24GEV_XSQ
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rule: x > 0.1
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- dataset: NNPDF_POS_2P24GEV_XSB
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rule: x > 0.1
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- {dataset: NNPDF_POS_100GEV_XUQ, maxlambda: 1e6}
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- {dataset: NNPDF_POS_100GEV_XUB, maxlambda: 1e6}
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- {dataset: NNPDF_POS_100GEV_XDQ, maxlambda: 1e6}
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- {dataset: NNPDF_POS_100GEV_XDB, maxlambda: 1e6}
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- {dataset: NNPDF_POS_100GEV_XSQ, maxlambda: 1e6}
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- {dataset: NNPDF_POS_100GEV_XSB, maxlambda: 1e6}
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- {dataset: NNPDF_POS_100GEV_XCQ, maxlambda: 1e6}
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- {dataset: NNPDF_POS_100GEV_XCB, maxlambda: 1e6}
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- {dataset: NNPDF_POS_100GEV_XGL, maxlambda: 1e6}
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integrability:
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integdatasets:

‎n3fit/src/n3fit/checks.py‎

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@@ -405,8 +405,8 @@ def check_consistent_basis(sum_rules, fitbasis, basis, theoryid, parameters):
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smallx = flavour_dict["smallx"]
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if smallx[0] > smallx[1]:
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raise CheckError(f"Wrong smallx range for flavour {name}: {smallx}")
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largex = flavour_dict["largex"]
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if largex[0] > largex[1]:
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largex = flavour_dict.get("largex")
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if largex is not None and largex[0] > largex[1]:
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raise CheckError(f"Wrong largex range for flavour {name}: {largex}")
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if name in flavs:
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raise CheckError(f"Repeated flavour name: {name}. Check basis dictionary")

‎nnpdf_data/nnpdf_data/commondata/NNPDF_POS_100GEV/kinematics_XCB.yaml‎

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bins:
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- x:
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min: null
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mid: 0.01
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max: null
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Q2:
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min: null
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mid: 10000.0
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max: null
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- x:
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min: null
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mid: 0.0129155
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max: null
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Q2:
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min: null
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mid: 10000.0
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max: null
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- x:
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min: null
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mid: 0.01668101
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max: null
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Q2:
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min: null
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mid: 10000.0
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max: null
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- x:
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min: null
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mid: 0.02154435
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max: null
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Q2:
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min: null
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mid: 10000.0
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max: null
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- x:
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min: null
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mid: 0.02782559
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max: null
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Q2:
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min: null
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mid: 10000.0
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max: null
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- x:
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min: null
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mid: 0.03593814
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max: null
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Q2:
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min: null
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mid: 10000.0
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max: null
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- x:
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min: null
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mid: 0.04641589
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max: null
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Q2:
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min: null
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mid: 10000.0
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max: null
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- x:
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min: null
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mid: 0.05994843
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max: null
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min: null
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mid: 10000.0
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max: null
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- x:
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min: null
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mid: 0.07742637
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max: null
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min: null
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mid: 10000.0
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max: null
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- x:
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min: null
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mid: 0.1
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max: null
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min: null
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mid: 10000.0
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max: null
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- x:
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mid: 0.18
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mid: 10000.0
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- x:
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mid: 0.26
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mid: 10000.0
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- x:
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min: null
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mid: 0.34
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mid: 10000.0
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- x:
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mid: 0.42
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mid: 10000.0
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- x:
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mid: 0.5
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- x:
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mid: 10000.0
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- x:
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mid: 0.66
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min: null
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mid: 10000.0
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max: null
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- x:
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mid: 0.74
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min: null
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mid: 10000.0
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- x:
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mid: 0.82
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min: null
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mid: 10000.0
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max: null
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min: null
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mid: 0.9
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max: null
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Q2:
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min: null
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mid: 10000.0
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max: null

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