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Correct typo + example runcard
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achiefa committed Jan 14, 2025
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212 changes: 212 additions & 0 deletions n3fit/runcards/examples/Basic_runcard_pc_covmat.yml
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#
# Configuration file for n3fit
#
######################################################################################
description: NNPDF4.0 ht with TCM - DIS (NC & CC) only

######################################################################################
dataset_inputs:
- {dataset: NMC_NC_NOTFIXED_EM-F2, frac: 0.75, variant: legacy_dw}
- {dataset: NMC_NC_NOTFIXED_P_EM-SIGMARED, frac: 0.75, variant: legacy}
- {dataset: SLAC_NC_NOTFIXED_P_EM-F2, frac: 0.75, variant: legacy_dw}
- {dataset: SLAC_NC_NOTFIXED_D_EM-F2, frac: 0.75, variant: legacy_dw}
- {dataset: BCDMS_NC_NOTFIXED_P_EM-F2, frac: 0.75, variant: legacy_dw}
- {dataset: BCDMS_NC_NOTFIXED_D_EM-F2, frac: 0.75, variant: legacy_dw}
- {dataset: CHORUS_CC_NOTFIXED_PB_NU-SIGMARED, frac: 0.75, variant: legacy_dw}
- {dataset: CHORUS_CC_NOTFIXED_PB_NB-SIGMARED, frac: 0.75, variant: legacy_dw}
- {dataset: NUTEV_CC_NOTFIXED_FE_NU-SIGMARED, cfac: [MAS], frac: 0.75, variant: legacy_dw}
- {dataset: NUTEV_CC_NOTFIXED_FE_NB-SIGMARED, cfac: [MAS], frac: 0.75, variant: legacy_dw}
- {dataset: HERA_NC_318GEV_EM-SIGMARED, frac: 0.75, variant: legacy}
- {dataset: HERA_NC_225GEV_EP-SIGMARED, frac: 0.75, variant: legacy}
- {dataset: HERA_NC_251GEV_EP-SIGMARED, frac: 0.75, variant: legacy}
- {dataset: HERA_NC_300GEV_EP-SIGMARED, frac: 0.75, variant: legacy}
- {dataset: HERA_NC_318GEV_EP-SIGMARED, frac: 0.75, variant: legacy}
- {dataset: HERA_CC_318GEV_EM-SIGMARED, frac: 0.75, variant: legacy}
- {dataset: HERA_CC_318GEV_EP-SIGMARED, frac: 0.75, variant: legacy}
- {dataset: HERA_NC_318GEV_EAVG_CHARM-SIGMARED, frac: 0.75, variant: legacy}
- {dataset: HERA_NC_318GEV_EAVG_BOTTOM-SIGMARED, frac: 0.75, variant: legacy}
- {dataset: DYE866_Z0_800GEV_DW_RATIO_PDXSECRATIO, frac: 0.75, variant: legacy}
- {dataset: DYE866_Z0_800GEV_PXSEC, frac: 0.75, variant: legacy}
- {dataset: DYE605_Z0_38P8GEV_DW_PXSEC, frac: 0.75, variant: legacy}
- {dataset: DYE906_Z0_120GEV_DW_PDXSECRATIO, frac: 0.75, cfac: [ACC], variant: legacy}
- {dataset: CDF_Z0_1P96TEV_ZRAP, frac: 0.75, variant: legacy}
- {dataset: D0_Z0_1P96TEV_ZRAP, frac: 0.75, variant: legacy}
- {dataset: D0_WPWM_1P96TEV_ASY, frac: 0.75, variant: legacy}
- {dataset: ATLAS_WPWM_7TEV_36PB_ETA, frac: 0.75, variant: legacy}
- {dataset: ATLAS_Z0_7TEV_36PB_ETA, frac: 0.75, variant: legacy}
- {dataset: ATLAS_Z0_7TEV_49FB_HIMASS, frac: 0.75, variant: legacy}
- {dataset: ATLAS_Z0_7TEV_LOMASS_M, frac: 0.75, variant: legacy}
- {dataset: ATLAS_WPWM_7TEV_46FB_CC-ETA, frac: 0.75, variant: legacy}
- {dataset: ATLAS_Z0_7TEV_46FB_CC-Y, frac: 0.75, variant: legacy}
- {dataset: ATLAS_Z0_7TEV_46FB_CF-Y, frac: 0.75, variant: legacy}
- {dataset: ATLAS_Z0_8TEV_HIMASS_M-Y, frac: 0.75, variant: legacy}
- {dataset: ATLAS_Z0_8TEV_LOWMASS_M-Y, frac: 0.75, variant: legacy}
- {dataset: ATLAS_Z0_13TEV_TOT, frac: 0.75, cfac: [NRM], variant: legacy}
- {dataset: ATLAS_WPWM_13TEV_TOT, frac: 0.75, cfac: [NRM], variant: legacy}
- {dataset: ATLAS_WJ_8TEV_WP-PT, frac: 0.75, variant: legacy}
- {dataset: ATLAS_WJ_8TEV_WM-PT, frac: 0.75, variant: legacy}
- {dataset: ATLAS_Z0J_8TEV_PT-M, frac: 0.75, variant: legacy_10}
- {dataset: ATLAS_Z0J_8TEV_PT-Y, frac: 0.75, variant: legacy_10}
- {dataset: ATLAS_TTBAR_7TEV_TOT_X-SEC, frac: 0.75, variant: legacy}
- {dataset: ATLAS_TTBAR_8TEV_TOT_X-SEC, frac: 0.75, variant: legacy}
- {dataset: ATLAS_TTBAR_13TEV_TOT_X-SEC, frac: 0.75, variant: legacy}
- {dataset: ATLAS_TTBAR_8TEV_LJ_DIF_YT-NORM, frac: 0.75, variant: legacy}
- {dataset: ATLAS_TTBAR_8TEV_LJ_DIF_YTTBAR-NORM, frac: 0.75, variant: legacy}
- {dataset: ATLAS_TTBAR_8TEV_2L_DIF_YTTBAR-NORM, frac: 0.75, variant: legacy}
- {dataset: ATLAS_1JET_8TEV_R06_PTY, frac: 0.75, variant: legacy_decorrelated}
- {dataset: ATLAS_2JET_7TEV_R06_M12Y, frac: 0.75, variant: legacy}
- {dataset: ATLAS_PH_13TEV_XSEC, frac: 0.75, cfac: [EWK], variant: legacy}
- {dataset: ATLAS_SINGLETOP_7TEV_TCHANNEL-XSEC, frac: 0.75, variant: legacy}
- {dataset: ATLAS_SINGLETOP_13TEV_TCHANNEL-XSEC, frac: 0.75, variant: legacy}
- {dataset: ATLAS_SINGLETOP_7TEV_T-Y-NORM, frac: 0.75, variant: legacy}
- {dataset: ATLAS_SINGLETOP_7TEV_TBAR-Y-NORM, frac: 0.75, variant: legacy}
- {dataset: ATLAS_SINGLETOP_8TEV_T-RAP-NORM, frac: 0.75, variant: legacy}
- {dataset: ATLAS_SINGLETOP_8TEV_TBAR-RAP-NORM, frac: 0.75, variant: legacy}
- {dataset: CMS_WPWM_7TEV_ELECTRON_ASY, frac: 0.75}
- {dataset: CMS_WPWM_7TEV_MUON_ASY, frac: 0.75, variant: legacy}
- {dataset: CMS_Z0_7TEV_DIMUON_2D, frac: 0.75}
- {dataset: CMS_WPWM_8TEV_MUON_Y, frac: 0.75, variant: legacy}
- {dataset: CMS_Z0J_8TEV_PT-Y, frac: 0.75, cfac: [NRM], variant: legacy_10}
- {dataset: CMS_2JET_7TEV_M12Y, frac: 0.75}
- {dataset: CMS_1JET_8TEV_PTY, frac: 0.75, variant: legacy}
- {dataset: CMS_TTBAR_7TEV_TOT_X-SEC, frac: 0.75, variant: legacy}
- {dataset: CMS_TTBAR_8TEV_TOT_X-SEC, frac: 0.75, variant: legacy}
- {dataset: CMS_TTBAR_13TEV_TOT_X-SEC, frac: 0.75, variant: legacy}
- {dataset: CMS_TTBAR_8TEV_LJ_DIF_YTTBAR-NORM, frac: 0.75, variant: legacy}
- {dataset: CMS_TTBAR_5TEV_TOT_X-SEC, frac: 0.75, variant: legacy}
- {dataset: CMS_TTBAR_8TEV_2L_DIF_MTTBAR-YT-NORM, frac: 0.75, variant: legacy}
- {dataset: CMS_TTBAR_13TEV_2L_DIF_YT, frac: 0.75, variant: legacy}
- {dataset: CMS_TTBAR_13TEV_LJ_2016_DIF_YTTBAR, frac: 0.75, variant: legacy}
- {dataset: CMS_SINGLETOP_7TEV_TCHANNEL-XSEC, frac: 0.75, variant: legacy}
- {dataset: CMS_SINGLETOP_8TEV_TCHANNEL-XSEC, frac: 0.75, variant: legacy}
- {dataset: CMS_SINGLETOP_13TEV_TCHANNEL-XSEC, frac: 0.75, variant: legacy}
- {dataset: LHCB_Z0_7TEV_DIELECTRON_Y, frac: 0.75}
- {dataset: LHCB_Z0_8TEV_DIELECTRON_Y, frac: 0.75}
- {dataset: LHCB_WPWM_7TEV_MUON_Y, frac: 0.75, cfac: [NRM]}
- {dataset: LHCB_Z0_7TEV_MUON_Y, frac: 0.75, cfac: [NRM]}
- {dataset: LHCB_WPWM_8TEV_MUON_Y, frac: 0.75, cfac: [NRM]}
- {dataset: LHCB_Z0_8TEV_MUON_Y, frac: 0.75, cfac: [NRM]}
- {dataset: LHCB_Z0_13TEV_DIMUON-Y, frac: 0.75}
- {dataset: LHCB_Z0_13TEV_DIELECTRON-Y, frac: 0.75}

################################################################################
datacuts:
t0pdfset: 240701-02-rs-nnpdf40-baseline
q2min: 2.5
w2min: 3.24

################################################################################
# NNLO QCD TRN evolution
theory:
theoryid: 708

theorycovmatconfig:
point_prescriptions: ["9 point", "power corrections"]
pc_parameters:
- {ht: H2p, yshift: [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0], nodes: [0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1]}
- {ht: H2d, yshift: [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0], nodes: [0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1]}
- {ht: HLp, yshift: [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0], nodes: [0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1]}
- {ht: HLd, yshift: [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0], nodes: [0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1]}
- {ht: H3p, yshift: [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0], nodes: [0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1]}
- {ht: H3d, yshift: [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0], nodes: [0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1]}
pc_included_procs: ["DIS NC", "DIS CC"]
pc_excluded_exps: [HERA_NC_318GEV_EAVG_CHARM-SIGMARED,
HERA_NC_318GEV_EAVG_BOTTOM-SIGMARED,]
pdf: 210619-n3fit-001
use_thcovmat_in_fitting: true
use_thcovmat_in_sampling: true

# For fits <= 4.0 multiplicative and additive uncertainties were sampled separately
# and thus the flag `separate_multiplicative` needs to be set to True
# sampling:
# separate_multiplicative: True

################################################################################
trvlseed: 591866982
nnseed: 945709987
mcseed: 519562661
genrep: true

################################################################################
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: 17000
positivity:
initial: 184.8
multiplier:
integrability:
initial: 10
multiplier:
stopping_patience: 0.1
layer_type: dense
dropout: 0.0
threshold_chi2: 3.5

fitting:
fitbasis: EVOL
savepseudodata: True
basis:
- {fl: sng, trainable: false, smallx: [1.089, 1.119], largex: [1.475, 3.119]}
- {fl: g, trainable: false, smallx: [0.7504, 1.098], largex: [2.814, 5.669]}
- {fl: v, trainable: false, smallx: [0.479, 0.7384], largex: [1.549, 3.532]}
- {fl: v3, trainable: false, smallx: [0.1073, 0.4397], largex: [1.733, 3.458]}
- {fl: v8, trainable: false, smallx: [0.5507, 0.7837], largex: [1.516, 3.356]}
- {fl: t3, trainable: false, smallx: [-0.4506, 0.9305], largex: [1.745, 3.424]}
- {fl: t8, trainable: false, smallx: [0.5877, 0.8687], largex: [1.522, 3.515]}
- {fl: t15, trainable: false, smallx: [1.089, 1.141], largex: [1.492, 3.222]}

################################################################################
positivity:
posdatasets:
# Positivity Lagrange Multiplier
- {dataset: NNPDF_POS_2P24GEV_F2U, maxlambda: 1e6}
- {dataset: NNPDF_POS_2P24GEV_F2D, maxlambda: 1e6}
- {dataset: NNPDF_POS_2P24GEV_F2S, maxlambda: 1e6}
- {dataset: NNPDF_POS_2P24GEV_FLL, maxlambda: 1e6}
- {dataset: NNPDF_POS_2P24GEV_DYU, maxlambda: 1e10}
- {dataset: NNPDF_POS_2P24GEV_DYD, maxlambda: 1e10}
- {dataset: NNPDF_POS_2P24GEV_DYS, maxlambda: 1e10}
- {dataset: NNPDF_POS_2P24GEV_F2C, maxlambda: 1e6}
# Positivity of MSbar PDFs
- {dataset: NNPDF_POS_2P24GEV_XUQ, maxlambda: 1e6}
- {dataset: NNPDF_POS_2P24GEV_XUB, maxlambda: 1e6}
- {dataset: NNPDF_POS_2P24GEV_XDQ, maxlambda: 1e6}
- {dataset: NNPDF_POS_2P24GEV_XDB, maxlambda: 1e6}
- {dataset: NNPDF_POS_2P24GEV_XSQ, maxlambda: 1e6}
- {dataset: NNPDF_POS_2P24GEV_XSB, maxlambda: 1e6}
- {dataset: NNPDF_POS_2P24GEV_XGL, maxlambda: 1e6}

added_filter_rules:
- dataset: NNPDF_POS_2P24GEV_FLL
rule: "x > 5.0e-7"
- dataset: NNPDF_POS_2P24GEV_F2C
rule: "x < 0.74"
- dataset: NNPDF_POS_2P24GEV_XGL
rule: "x > 0.1"
- dataset: NNPDF_POS_2P24GEV_XUQ
rule: "x > 0.1"
- dataset: NNPDF_POS_2P24GEV_XUB
rule: "x > 0.1"
- dataset: NNPDF_POS_2P24GEV_XDQ
rule: "x > 0.1"
- dataset: NNPDF_POS_2P24GEV_XDB
rule: "x > 0.1"
- dataset: NNPDF_POS_2P24GEV_XSQ
rule: "x > 0.1"
- dataset: NNPDF_POS_2P24GEV_XSB
rule: "x > 0.1"

integrability:
integdatasets:
- {dataset: NNPDF_INTEG_3GEV_XT8, maxlambda: 1e2}
- {dataset: NNPDF_INTEG_3GEV_XT3, maxlambda: 1e2}

################################################################################
debug: false
maxcores: 8
4 changes: 2 additions & 2 deletions validphys2/src/validphys/theorycovariance/construction.py
Original file line number Diff line number Diff line change
Expand Up @@ -335,7 +335,7 @@ def covs_pt_prescrip(
point_prescription,
pdf: PDF,
power_corr_dict,
pc_included_prosc,
pc_included_procs,
pc_excluded_exps,
):
"""Produces the sub-matrices of the theory covariance matrix according
Expand Down Expand Up @@ -380,7 +380,7 @@ def covs_pt_prescrip(

is_excluded_exp = any(name in pc_excluded_exps for name in [exp_name1, exp_name2])
is_included_proc = any(
proc not in pc_included_prosc for proc in [process_type1, process_type2]
proc not in pc_included_procs for proc in [process_type1, process_type2]
)
if not (is_excluded_exp or is_included_proc):
deltas1 = compute_deltas_pc(data_spec1, pdf, power_corr_dict)
Expand Down

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