From b757cb797949055e530f8cd9ac67b4f90b49609f Mon Sep 17 00:00:00 2001 From: Maryam Soleymaninia <147120752+Maryamgithub63@users.noreply.github.com> Date: Mon, 15 Jun 2026 18:37:34 +0400 Subject: [PATCH 1/4] Add HAPS-hFF1.0 fit cards --- config/HAPS-hFF1.0-NLO.yaml | 126 +++++++++++++++++++++++++++++++++++ config/HAPS-hFF1.0-NNLO.yaml | 126 +++++++++++++++++++++++++++++++++++ 2 files changed, 252 insertions(+) create mode 100644 config/HAPS-hFF1.0-NLO.yaml create mode 100644 config/HAPS-hFF1.0-NNLO.yaml diff --git a/config/HAPS-hFF1.0-NLO.yaml b/config/HAPS-hFF1.0-NLO.yaml new file mode 100644 index 000000000..4daa392d5 --- /dev/null +++ b/config/HAPS-hFF1.0-NLO.yaml @@ -0,0 +1,126 @@ +# +# SHK22.h: Neural network QCD analysis of charged hadron fragmentation functions in the presence of SIDIS data, +# Maryam Soleymaninia, Hadi Hashamipour, Hamzeh Khanpour, +# Published in: Phys.Rev.D 105 (2022) 11, 11; +# e-Print: 2202.10779 [hep-ph] +# +################################# +# Predictions +################################# +Predictions: + # Perturbative order, 0: LO, 1: NLO + perturbative order: 1 + + # Initial scale in GeV to be used for the DGLAP evolution of the FFs. + mu0: 5 + + # Quark thresholds + thresholds: [0, 0, 0, 1.51, 4.92] + + # Strong coupling + alphas: + aref: 0.118 + Qref: 91.1876 + + # Fine-structure constant + alphaem: + aref: 0.00776578395589 + Qref: 91.1876 + + # APFEL++ grid + xgrid: + - [100, 1e-2, 3] + - [60, 2e-1, 3] + - [40, 8e-1, 3] + + # PDF set + pdfset: + name: NNPDF40_nlo_pch_as_01180 + member: -1 #N>=0 for a specific member (0 for central) ; N<0 for all members to be used randomly(flat dist) + +################################# +# Optimiser +################################# +# Parameters of the optimiser managed by ceres-solver +Optimizer: + max_num_iterations: 3000 + chi2_tolerance: 3 + +################################# +# NNAD +################################# +NNAD: + # Initialisation seed + seed: 0 + + # Architecture + architecture: [1, 20, 7] + + # The output function can be either the activation function of the + # NN (0), or linear (1), or quadratic (2). If this entry is absent a + # linear function is assumed. + output function: 2 + + # The flavour map gives the the specific combinations of + # distributions in the physical-basis (d, u, s, ..) to be fitted to + # the data. The number of combinations has to match the number of + # nodes of the output layer of the NN given in the + # architecture. When defining the flavour map, one should keep in + # mind that the code computes predictions using the QCD-evolution + # basis (Sigma, V, T3, ...). Therefore, in general, + # QCD-evolution-like combinations should be preferred. Moreover, the + # distributions are assumed to be for the positive charge of the a + # given hadronic species. The negative distributions are derived + # using charge conjugation (q->qbar). + + # tb bb cb sb ub db g d u s c b t # Combinations to be fitted (i.e. output of the NN): + flavour map: [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, # - ub + 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, # - db + sb + 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, # - g + 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, # - d+s + 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, # - u + 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, # - c+ + 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] # - b+ + +################################# +# Data +################################# +Data: + # Seed used for the replica generation and the splitting between + # training and validation (do not use a too large number here in + # order to avoid correlation in the replica generation). + seed: 2 + + # Hadronic species being fitted. This is used to make sure that all + # data sets included in the fit actually refer to the correct + # species. + hadron: H + + # Datasets to be included in the fit along with specific cuts and + # traning fraction. Each single dataset can implement an arbitrary + # number of cuts determined by the name of the appropriate function + # (e.g. zcut) and the allowed range. + sets: + - {name: "COMPASS $H^+$ 2026", file: "COMPASS_H_PLUS_2026.yaml", cuts: [{name: Qcut, min: 1.4, max: 50}], training fraction: 0.5} + - {name: "COMPASS $H^-$ 2026", file: "COMPASS_H_MINUS_2026.yaml", cuts: [{name: Qcut, min: 1.4, max: 50}], training fraction: 0.5} + - {name: "COMPASS $H^+$ 2025", file: "COMPASS_H_PLUS_2025.yaml", cuts: [{name: Qcut, min: 1.4, max: 50}], training fraction: 0.5} + - {name: "COMPASS $H^-$ 2025", file: "COMPASS_H_MINUS_2025.yaml", cuts: [{name: Qcut, min: 1.4, max: 50}], training fraction: 0.5} + #- {name: "COMPASS $H^-$", file: "COMPASS_H_MINUS.yaml", cuts: [{name: Qcut, min: 2.0, max: 50}], training fraction: 0.5} + #- {name: "COMPASS $H^+$", file: "COMPASS_H_PLUS.yaml", cuts: [{name: Qcut, min: 2.0, max: 50}], training fraction: 0.5} + - {name: "TASSO 14 GeV $H^\\pm$", file: "TASSO_14_H_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "TASSO 22 GeV $H^\\pm$", file: "TASSO_22_H_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + #- {name: "TASSO 35 GeV $H^\\pm$", file: "TASSO_35_H_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "TASSO 44 GeV $H^\\pm$", file: "TASSO_44_H_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "TPC $H^\\pm$", file: "TPC_H_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "ALEPH $H^\\pm$", file: "ALEPH_H_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "DELPHI total $H^\\pm$", file: "DELPHI_H_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "DELPHI $uds$ $H^\\pm$", file: "DELPHI_H_PLUS_MINUS_UDS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "DELPHI bottom $H^\\pm$", file: "DELPHI_H_PLUS_MINUS_B.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "OPAL total $H^\\pm$", file: "OPAL_H_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "OPAL $uds$ $H^\\pm$", file: "OPAL_H_PLUS_MINUS_UDS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "OPAL charm $H^\\pm$", file: "OPAL_H_PLUS_MINUS_C.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "OPAL bottom $H^\\pm$", file: "OPAL_H_PLUS_MINUS_B.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "SLD total $H^\\pm$", file: "SLD_H_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "SLD $uds$ $H^\\pm$", file: "SLD_H_PLUS_MINUS_UDS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "SLD charm $H^\\pm$", file: "SLD_H_PLUS_MINUS_C.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "SLD bottom $H^\\pm$", file: "SLD_H_PLUS_MINUS_B.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} diff --git a/config/HAPS-hFF1.0-NNLO.yaml b/config/HAPS-hFF1.0-NNLO.yaml new file mode 100644 index 000000000..66e28ce05 --- /dev/null +++ b/config/HAPS-hFF1.0-NNLO.yaml @@ -0,0 +1,126 @@ +# +# SHK22.h: Neural network QCD analysis of charged hadron fragmentation functions in the presence of SIDIS data, +# Maryam Soleymaninia, Hadi Hashamipour, Hamzeh Khanpour, +# Published in: Phys.Rev.D 105 (2022) 11, 11; +# e-Print: 2202.10779 [hep-ph] +# +################################# +# Predictions +################################# +Predictions: + # Perturbative order, 0: LO, 1: NLO + perturbative order: 2 + + # Initial scale in GeV to be used for the DGLAP evolution of the FFs. + mu0: 5 + + # Quark thresholds + thresholds: [0, 0, 0, 1.51, 4.92] + + # Strong coupling + alphas: + aref: 0.118 + Qref: 91.1876 + + # Fine-structure constant + alphaem: + aref: 0.00776578395589 + Qref: 91.1876 + + # APFEL++ grid + xgrid: + - [100, 1e-2, 3] + - [60, 2e-1, 3] + - [40, 8e-1, 3] + + # PDF set + pdfset: + name: NNPDF40_nnlo_pch_as_01180 + member: -1 #N>=0 for a specific member (0 for central) ; N<0 for all members to be used randomly(flat dist) + +################################# +# Optimiser +################################# +# Parameters of the optimiser managed by ceres-solver +Optimizer: + max_num_iterations: 3000 + chi2_tolerance: 3 + +################################# +# NNAD +################################# +NNAD: + # Initialisation seed + seed: 0 + + # Architecture + architecture: [1, 20, 7] + + # The output function can be either the activation function of the + # NN (0), or linear (1), or quadratic (2). If this entry is absent a + # linear function is assumed. + output function: 2 + + # The flavour map gives the the specific combinations of + # distributions in the physical-basis (d, u, s, ..) to be fitted to + # the data. The number of combinations has to match the number of + # nodes of the output layer of the NN given in the + # architecture. When defining the flavour map, one should keep in + # mind that the code computes predictions using the QCD-evolution + # basis (Sigma, V, T3, ...). Therefore, in general, + # QCD-evolution-like combinations should be preferred. Moreover, the + # distributions are assumed to be for the positive charge of the a + # given hadronic species. The negative distributions are derived + # using charge conjugation (q->qbar). + + # tb bb cb sb ub db g d u s c b t # Combinations to be fitted (i.e. output of the NN): + flavour map: [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, # - ub + 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, # - db + sb + 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, # - g + 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, # - d+s + 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, # - u + 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, # - c+ + 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] # - b+ + +################################# +# Data +################################# +Data: + # Seed used for the replica generation and the splitting between + # training and validation (do not use a too large number here in + # order to avoid correlation in the replica generation). + seed: 2 + + # Hadronic species being fitted. This is used to make sure that all + # data sets included in the fit actually refer to the correct + # species. + hadron: H + + # Datasets to be included in the fit along with specific cuts and + # traning fraction. Each single dataset can implement an arbitrary + # number of cuts determined by the name of the appropriate function + # (e.g. zcut) and the allowed range. + sets: + - {name: "COMPASS $H^+$ 2026", file: "COMPASS_H_PLUS_2026.yaml", cuts: [{name: Qcut, min: 1.4, max: 50}], training fraction: 0.5} + - {name: "COMPASS $H^-$ 2026", file: "COMPASS_H_MINUS_2026.yaml", cuts: [{name: Qcut, min: 1.4, max: 50}], training fraction: 0.5} + - {name: "COMPASS $H^+$ 2025", file: "COMPASS_H_PLUS_2025.yaml", cuts: [{name: Qcut, min: 1.4, max: 50}], training fraction: 0.5} + - {name: "COMPASS $H^-$ 2025", file: "COMPASS_H_MINUS_2025.yaml", cuts: [{name: Qcut, min: 1.4, max: 50}], training fraction: 0.5} + #- {name: "COMPASS $H^-$", file: "COMPASS_H_MINUS.yaml", cuts: [{name: Qcut, min: 2.0, max: 50}], training fraction: 0.5} + #- {name: "COMPASS $H^+$", file: "COMPASS_H_PLUS.yaml", cuts: [{name: Qcut, min: 2.0, max: 50}], training fraction: 0.5} + - {name: "TASSO 14 GeV $H^\\pm$", file: "TASSO_14_H_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "TASSO 22 GeV $H^\\pm$", file: "TASSO_22_H_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + #- {name: "TASSO 35 GeV $H^\\pm$", file: "TASSO_35_H_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "TASSO 44 GeV $H^\\pm$", file: "TASSO_44_H_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "TPC $H^\\pm$", file: "TPC_H_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "ALEPH $H^\\pm$", file: "ALEPH_H_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "DELPHI total $H^\\pm$", file: "DELPHI_H_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "DELPHI $uds$ $H^\\pm$", file: "DELPHI_H_PLUS_MINUS_UDS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "DELPHI bottom $H^\\pm$", file: "DELPHI_H_PLUS_MINUS_B.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "OPAL total $H^\\pm$", file: "OPAL_H_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "OPAL $uds$ $H^\\pm$", file: "OPAL_H_PLUS_MINUS_UDS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "OPAL charm $H^\\pm$", file: "OPAL_H_PLUS_MINUS_C.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "OPAL bottom $H^\\pm$", file: "OPAL_H_PLUS_MINUS_B.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "SLD total $H^\\pm$", file: "SLD_H_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "SLD $uds$ $H^\\pm$", file: "SLD_H_PLUS_MINUS_UDS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "SLD charm $H^\\pm$", file: "SLD_H_PLUS_MINUS_C.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "SLD bottom $H^\\pm$", file: "SLD_H_PLUS_MINUS_B.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} From 5d60d33894004d42d92d84585facc8e68d1f26ed Mon Sep 17 00:00:00 2001 From: Maryam Soleymaninia <147120752+Maryamgithub63@users.noreply.github.com> Date: Mon, 15 Jun 2026 18:41:49 +0400 Subject: [PATCH 2/4] Add HAPS-hFF1.0 COMPASS data files --- data/COMPASS_H_MINUS_2025.yaml | 2440 +++++++++++++++++++++++++++++++ data/COMPASS_H_MINUS_2026.yaml | 2472 ++++++++++++++++++++++++++++++++ data/COMPASS_H_PLUS_2025.yaml | 2440 +++++++++++++++++++++++++++++++ data/COMPASS_H_PLUS_2026.yaml | 2472 ++++++++++++++++++++++++++++++++ 4 files changed, 9824 insertions(+) create mode 100644 data/COMPASS_H_MINUS_2025.yaml create mode 100644 data/COMPASS_H_MINUS_2026.yaml create mode 100644 data/COMPASS_H_PLUS_2025.yaml create mode 100644 data/COMPASS_H_PLUS_2026.yaml diff --git a/data/COMPASS_H_MINUS_2025.yaml b/data/COMPASS_H_MINUS_2025.yaml new file mode 100644 index 000000000..9165cf10a --- /dev/null +++ b/data/COMPASS_H_MINUS_2025.yaml @@ -0,0 +1,2440 @@ +dependent_variables: + - header: {title: COMPASS $H^-$ Multiplicities 2025} + qualifiers: + - {name: process, value: SIDIS} + - {name: observable, value: dsigma/dxdydz} + - {name: target_isoscalarity, value: 1.0} + - {name: prefactor, value: 1} + - {name: Vs, value: 17.35} + - {name: Q, low: 1, high: 7.745966692414834, integrate: true} + - {name: x, low: 0.004, high: 0.7, integrate: true} + - {name: z, low: 0.2, high: 0.85, integrate: true} + - {name: PS_reduction, W: 5, ymin: 0.1, ymax: 0.7} + - {name: hadron, value: H} + - {name: charge, value: -1} + values: + - errors: + - {label: unc, value: 0.0805} + - {label: add, value: 0.04026} + value: 2.5299 + - errors: + - {label: unc, value: 0.05664} + - {label: add, value: 0.04035} + value: 1.74937 + - errors: + - {label: unc, value: 0.04147} + - {label: add, value: 0.04083} + value: 1.23602 + - errors: + - {label: unc, value: 0.03148} + - {label: add, value: 0.04134} + value: 0.89865 + - errors: + - {label: unc, value: 0.02425} + - {label: add, value: 0.04255} + value: 0.65312 + - errors: + - {label: unc, value: 0.019} + - {label: add, value: 0.04421} + value: 0.47351 + - errors: + - {label: unc, value: 0.01694} + - {label: add, value: 0.04712} + value: 0.35856 + - errors: + - {label: unc, value: 0.01696} + - {label: add, value: 0.05166} + value: 0.27656 + - errors: + - {label: unc, value: 0.08472} + - {label: add, value: 0.04404} + value: 2.504 + - errors: + - {label: unc, value: 0.05917} + - {label: add, value: 0.0444} + value: 1.71572 + - errors: + - {label: unc, value: 0.04326} + - {label: add, value: 0.04463} + value: 1.22972 + - errors: + - {label: unc, value: 0.03198} + - {label: add, value: 0.04454} + value: 0.86701 + - errors: + - {label: unc, value: 0.02746} + - {label: add, value: 0.04526} + value: 0.63477 + - errors: + - {label: unc, value: 0.03581} + - {label: add, value: 0.04028} + value: 0.64203 + - errors: + - {label: unc, value: 0.02534} + - {label: add, value: 0.04182} + value: 0.50812 + - errors: + - {label: unc, value: 0.02047} + - {label: add, value: 0.04573} + value: 0.36368 + - errors: + - {label: unc, value: 0.01615} + - {label: add, value: 0.05131} + value: 0.24839 + - errors: + - {label: unc, value: 0.01662} + - {label: add, value: 0.06064} + value: 0.23481 + - errors: + - {label: unc, value: 0.01324} + - {label: add, value: 0.06644} + value: 0.16485 + - errors: + - {label: unc, value: 0.01158} + - {label: add, value: 0.09305} + value: 0.11718 + - errors: + - {label: unc, value: 0.00796} + - {label: add, value: 0.13973} + value: 0.0659 + - errors: + - {label: unc, value: 0.03729} + - {label: add, value: 0.03756} + value: 1.2187 + - errors: + - {label: unc, value: 0.02714} + - {label: add, value: 0.03827} + value: 0.8849 + - errors: + - {label: unc, value: 0.02062} + - {label: add, value: 0.03937} + value: 0.6486 + - errors: + - {label: unc, value: 0.01626} + - {label: add, value: 0.04089} + value: 0.48502 + - errors: + - {label: unc, value: 0.01299} + - {label: add, value: 0.0439} + value: 0.3566 + - errors: + - {label: unc, value: 0.01077} + - {label: add, value: 0.04866} + value: 0.26553 + - errors: + - {label: unc, value: 0.00915} + - {label: add, value: 0.05371} + value: 0.20315 + - errors: + - {label: unc, value: 0.00778} + - {label: add, value: 0.06373} + value: 0.14675 + - errors: + - {label: unc, value: 0.00746} + - {label: add, value: 0.07817} + value: 0.11697 + - errors: + - {label: unc, value: 0.00723} + - {label: add, value: 0.13271} + value: 0.07101 + - errors: + - {label: unc, value: 0.07236} + - {label: add, value: 0.03778} + value: 2.47873 + - errors: + - {label: unc, value: 0.04941} + - {label: add, value: 0.03789} + value: 1.70123 + - errors: + - {label: unc, value: 0.03558} + - {label: add, value: 0.03818} + value: 1.20698 + - errors: + - {label: unc, value: 0.026} + - {label: add, value: 0.03866} + value: 0.86231 + - errors: + - {label: unc, value: 0.01949} + - {label: add, value: 0.03949} + value: 0.62543 + - errors: + - {label: unc, value: 0.01511} + - {label: add, value: 0.04054} + value: 0.46586 + - errors: + - {label: unc, value: 0.012} + - {label: add, value: 0.04295} + value: 0.34235 + - 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{value: 34.91861} + - {value: 34.91861} + - {value: 34.91861} + - {value: 34.91861} + - {value: 34.91861} diff --git a/data/COMPASS_H_PLUS_2025.yaml b/data/COMPASS_H_PLUS_2025.yaml new file mode 100644 index 000000000..664916a85 --- /dev/null +++ b/data/COMPASS_H_PLUS_2025.yaml @@ -0,0 +1,2440 @@ +dependent_variables: + - header: {title: COMPASS $H^+$ Multiplicities 2025} + qualifiers: + - {name: process, value: SIDIS} + - {name: observable, value: dsigma/dxdydz} + - {name: target_isoscalarity, value: 1.0} + - {name: prefactor, value: 1} + - {name: Vs, value: 17.35} + - {name: Q, low: 1, high: 7.745966692414834, integrate: true} + - {name: x, low: 0.004, high: 0.7, integrate: true} + - {name: z, low: 0.2, high: 0.85, integrate: true} + - {name: PS_reduction, W: 5, ymin: 0.1, ymax: 0.7} + - {name: hadron, value: H} + - {name: charge, value: 1} + values: + - errors: + - {label: unc, value: 0.08948} + - {label: add, value: 0.04008} + value: 2.8375 + - errors: + - {label: unc, value: 0.06394} + - 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[100, 1e-2, 3] + - [60, 2e-1, 3] + - [40, 8e-1, 3] + + # PDF set + pdfset: + name: NNPDF40_nlo_pch_as_01180 + member: -1 #N>=0 for a specific member (0 for central) ; N<0 for all members to be used randomly(flat dist) + +################################# +# Optimiser +################################# +# Parameters of the optimiser managed by ceres-solver +Optimizer: + max_num_iterations: 3000 + chi2_tolerance: 3 +################################# +# NNAD +################################# +NNAD: + # Initialisation seed + seed: 4 + # Architecture + architecture: [1, 20, 7] + # The output function can be either the activation function of the + # NN (0), or linear (1), or quadratic (2). If this entry is absent a + # linear function is assumed. + output function: 2 + # The flavour map gives the the specific combinations of + # distributions in the physical-basis (d, u, s, ..) to be fitted to + # the data. The number of combinations has to match the number of + # nodes of the output layer of the NN given in the + # architecture. When defining the flavour map, one should keep in + # mind that the code computes predictions using the QCD-evolution + # basis (Sigma, V, T3, ...). Therefore, in general, + # QCD-evolution-like combinations should be preferred. Moreover, the + # distributions are assumed to be for the positive charge of the a + # given hadronic species. The negative distributions are derived + # using charge conjugation (q->qbar). + + # tb bb cb sb ub db g d u s c b t # Combinations to be fitted (i.e. output of the NN): + flavour map: [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, # - sb + 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, # - g + 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, # - s = ubar + 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, # - u + 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, # - d+ + 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, # - c+ + 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] # - b+ + #tb bb cb sb ub db g d u s c b t + combine: true +################################# +# Data +################################# +Data: + # Seed used for the replica generation and the splitting between + # training and validation (do not use a too large number here in + # order to avoid correlation in the replica generation). + seed: 0 + # Hadronic species being fitted. This is used to make sure that all + # data sets included in the fit actually refer to the correct + # species. + hadron: KA + # Datasets to be included in the fit along with specific cuts and + # traning fraction. Each single dataset can implement an arbitrary + # number of cuts determined by the name of the appropriate function + # (e.g. zcut) and the allowed range. + sets: + - {name: "COMPASS $K^+$ 2026", file: "COMPASS_KA_PLUS_2026.yaml", cuts: [{name: Qcut, min: 1.7, max: 50}], training fraction: 0.5} + - {name: "COMPASS $K^-$ 2026", file: "COMPASS_KA_MINUS_2026.yaml", cuts: [{name: Qcut, min: 1.7, max: 50}], training fraction: 0.5} + - {name: "COMPASS $K^+$ 2025", file: "COMPASS_KA_PLUS_2025.yaml", cuts: [{name: Qcut, min: 1.7, max: 50}], training fraction: 0.5} + - {name: "COMPASS $K^-$ 2025", file: "COMPASS_KA_MINUS_2025.yaml", cuts: [{name: Qcut, min: 1.7, max: 50}], training fraction: 0.5} + - {name: "HERMES $K^-$ deuteron", file: "HERMES_KA_MINUS_DEUTERON.yaml", cuts: [{name: Qcut, min: 1.7, max: 50}, {name: zcut, min: 0.2, max: 0.8}], training fraction: 0.5} + - {name: "HERMES $K^-$ proton", file: "HERMES_KA_MINUS_PROTON.yaml", cuts: [{name: Qcut, min: 1.7, max: 50}, {name: zcut, min: 0.2, max: 0.8}], training fraction: 0.5} + - {name: "HERMES $K^+$ deuteron", file: "HERMES_KA_PLUS_DEUTERON.yaml", cuts: [{name: Qcut, min: 1.7, max: 50}, {name: zcut, min: 0.2, max: 0.8}], training fraction: 0.5} + - {name: "HERMES $K^+$ proton", file: "HERMES_KA_PLUS_PROTON.yaml", cuts: [{name: Qcut, min: 1.7, max: 50}, {name: zcut, min: 0.2, max: 0.8}], training fraction: 0.5} +# - {name: "COMPASS $K^-$", file: "COMPASS_KA_MINUS_DECORR.yaml", cuts: [{name: Qcut, min: 1.7, max: 50}], training fraction: 0.5} +# - {name: "COMPASS $K^+$", file: "COMPASS_KA_PLUS_DECORR.yaml", cuts: [{name: Qcut, min: 1.7, max: 50}], training fraction: 0.5} + - {name: "BELLE $K^\\pm$", file: "BELLE_KA_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "BABAR conventional $K^\\pm$", file: "BABAR_KA_PLUS_MINUS_CONVENTIONAL.yaml", cuts: [{name: zcut, min: 0.2, max: 0.9}], training fraction: 0.5} + - {name: "TASSO 12 GeV $K^\\pm$", file: "TASSO_12_KA_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "TASSO 14 GeV $K^\\pm$", file: "TASSO_14_KA_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "TASSO 22 GeV $K^\\pm$", file: "TASSO_22_KA_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "TPC $K^\\pm$", file: "TPC_KA_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "TASSO 30 GeV $K^\\pm$", file: "TASSO_30_KA_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "TASSO 34 GeV $K^\\pm$", file: "TASSO_34_KA_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "TASSO 44 GeV $K^\\pm$", file: "TASSO_44_KA_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "TOPAZ $K^\\pm$", file: "TOPAZ_KA_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "ALEPH $K^\\pm$", file: "ALEPH_KA_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "DELPHI total $K^\\pm$", file: "DELPHI_KA_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "DELPHI $uds$ $K^\\pm$", file: "DELPHI_KA_PLUS_MINUS_UDS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "DELPHI bottom $K^\\pm$", file: "DELPHI_KA_PLUS_MINUS_B.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "OPAL $K^\\pm$", file: "OPAL_KA_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "SLD total $K^\\pm$", file: "SLD_KA_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "SLD $uds$ $K^\\pm$", file: "SLD_KA_PLUS_MINUS_UDS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + #- {name: "SLD charm $K^\\pm$", file: "SLD_KA_PLUS_MINUS_C.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "SLD bottom $K^\\pm$", file: "SLD_KA_PLUS_MINUS_B.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} diff --git a/config/HAPS-KaFF1.0-NNLO.yaml b/config/HAPS-KaFF1.0-NNLO.yaml new file mode 100644 index 000000000..4600ee59e --- /dev/null +++ b/config/HAPS-KaFF1.0-NNLO.yaml @@ -0,0 +1,121 @@ +################################# +# Predictions +################################# +Predictions: + # Perturbative order, 0: LO, 1: NLO + perturbative order: 2 + + # Initial scale in GeV to be used for the DGLAP evolution of the FFs. + mu0: 5 + + # Quark thresholds + thresholds: [0, 0, 0, 1.51, 4.92] + + # Strong coupling + alphas: + aref: 0.118 + Qref: 91.1876 + + # Fine-structure constant + alphaem: + aref: 0.00776578395589 + Qref: 91.1876 + + # APFEL++ grid + xgrid: + - [100, 1e-2, 3] + - [60, 2e-1, 3] + - [40, 8e-1, 3] + + # PDF set + pdfset: + name: NNPDF40_nnlo_pch_as_01180 + member: -1 #N>=0 for a specific member (0 for central) ; N<0 for all members to be used randomly(flat dist) + +################################# +# Optimiser +################################# +# Parameters of the optimiser managed by ceres-solver +Optimizer: + max_num_iterations: 3000 + chi2_tolerance: 3 +################################# +# NNAD +################################# +NNAD: + # Initialisation seed + seed: 4 + # Architecture + architecture: [1, 20, 7] + # The output function can be either the activation function of the + # NN (0), or linear (1), or quadratic (2). If this entry is absent a + # linear function is assumed. + output function: 2 + # The flavour map gives the the specific combinations of + # distributions in the physical-basis (d, u, s, ..) to be fitted to + # the data. The number of combinations has to match the number of + # nodes of the output layer of the NN given in the + # architecture. When defining the flavour map, one should keep in + # mind that the code computes predictions using the QCD-evolution + # basis (Sigma, V, T3, ...). Therefore, in general, + # QCD-evolution-like combinations should be preferred. Moreover, the + # distributions are assumed to be for the positive charge of the a + # given hadronic species. The negative distributions are derived + # using charge conjugation (q->qbar). + + # tb bb cb sb ub db g d u s c b t # Combinations to be fitted (i.e. output of the NN): + flavour map: [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, # - sb + 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, # - g + 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, # - s = ubar + 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, # - u + 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, # - d+ + 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, # - c+ + 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] # - b+ + #tb bb cb sb ub db g d u s c b t + combine: true +################################# +# Data +################################# +Data: + # Seed used for the replica generation and the splitting between + # training and validation (do not use a too large number here in + # order to avoid correlation in the replica generation). + seed: 0 + # Hadronic species being fitted. This is used to make sure that all + # data sets included in the fit actually refer to the correct + # species. + hadron: KA + # Datasets to be included in the fit along with specific cuts and + # traning fraction. Each single dataset can implement an arbitrary + # number of cuts determined by the name of the appropriate function + # (e.g. zcut) and the allowed range. + sets: + - {name: "COMPASS $K^+$ 2026", file: "COMPASS_KA_PLUS_2026.yaml", cuts: [{name: Qcut, min: 1.7, max: 50}], training fraction: 0.5} + - {name: "COMPASS $K^-$ 2026", file: "COMPASS_KA_MINUS_2026.yaml", cuts: [{name: Qcut, min: 1.7, max: 50}], training fraction: 0.5} + - {name: "COMPASS $K^+$ 2025", file: "COMPASS_KA_PLUS_2025.yaml", cuts: [{name: Qcut, min: 1.7, max: 50}], training fraction: 0.5} + - {name: "COMPASS $K^-$ 2025", file: "COMPASS_KA_MINUS_2025.yaml", cuts: [{name: Qcut, min: 1.7, max: 50}], training fraction: 0.5} + - {name: "HERMES $K^-$ deuteron", file: "HERMES_KA_MINUS_DEUTERON.yaml", cuts: [{name: Qcut, min: 1.7, max: 50}, {name: zcut, min: 0.2, max: 0.8}], training fraction: 0.5} + - {name: "HERMES $K^-$ proton", file: "HERMES_KA_MINUS_PROTON.yaml", cuts: [{name: Qcut, min: 1.7, max: 50}, {name: zcut, min: 0.2, max: 0.8}], training fraction: 0.5} + - {name: "HERMES $K^+$ deuteron", file: "HERMES_KA_PLUS_DEUTERON.yaml", cuts: [{name: Qcut, min: 1.7, max: 50}, {name: zcut, min: 0.2, max: 0.8}], training fraction: 0.5} + - {name: "HERMES $K^+$ proton", file: "HERMES_KA_PLUS_PROTON.yaml", cuts: [{name: Qcut, min: 1.7, max: 50}, {name: zcut, min: 0.2, max: 0.8}], training fraction: 0.5} +# - {name: "COMPASS $K^-$", file: "COMPASS_KA_MINUS_DECORR.yaml", cuts: [{name: Qcut, min: 1.7, max: 50}], training fraction: 0.5} +# - {name: "COMPASS $K^+$", file: "COMPASS_KA_PLUS_DECORR.yaml", cuts: [{name: Qcut, min: 1.7, max: 50}], training fraction: 0.5} + - {name: "BELLE $K^\\pm$", file: "BELLE_KA_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "BABAR conventional $K^\\pm$", file: "BABAR_KA_PLUS_MINUS_CONVENTIONAL.yaml", cuts: [{name: zcut, min: 0.2, max: 0.9}], training fraction: 0.5} + - {name: "TASSO 12 GeV $K^\\pm$", file: "TASSO_12_KA_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "TASSO 14 GeV $K^\\pm$", file: "TASSO_14_KA_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "TASSO 22 GeV $K^\\pm$", file: "TASSO_22_KA_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "TPC $K^\\pm$", file: "TPC_KA_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "TASSO 30 GeV $K^\\pm$", file: "TASSO_30_KA_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "TASSO 34 GeV $K^\\pm$", file: "TASSO_34_KA_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "TASSO 44 GeV $K^\\pm$", file: "TASSO_44_KA_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "TOPAZ $K^\\pm$", file: "TOPAZ_KA_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "ALEPH $K^\\pm$", file: "ALEPH_KA_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "DELPHI total $K^\\pm$", file: "DELPHI_KA_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "DELPHI $uds$ $K^\\pm$", file: "DELPHI_KA_PLUS_MINUS_UDS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "DELPHI bottom $K^\\pm$", file: "DELPHI_KA_PLUS_MINUS_B.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "OPAL $K^\\pm$", file: "OPAL_KA_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "SLD total $K^\\pm$", file: "SLD_KA_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "SLD $uds$ $K^\\pm$", file: "SLD_KA_PLUS_MINUS_UDS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + #- {name: "SLD charm $K^\\pm$", file: "SLD_KA_PLUS_MINUS_C.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "SLD bottom $K^\\pm$", file: "SLD_KA_PLUS_MINUS_B.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} diff --git a/config/HAPS-PiFF1.0-NLO.yaml b/config/HAPS-PiFF1.0-NLO.yaml new file mode 100644 index 000000000..9e9157bb7 --- /dev/null +++ b/config/HAPS-PiFF1.0-NLO.yaml @@ -0,0 +1,126 @@ +################################# +# Predictions +################################# +Predictions: + # Perturbative order, 0: LO, 1: NLO + perturbative order: 1 + + # Initial scale in GeV to be used for the DGLAP evolution of the FFs. + mu0: 5 + + # Quark thresholds + thresholds: [0, 0, 0, 1.51, 4.92] + + # Strong coupling + alphas: + aref: 0.118 + Qref: 91.1876 + + # Fine-structure constant + alphaem: + aref: 0.00776578395589 + Qref: 91.1876 + + # APFEL++ grid + xgrid: + - [100, 1e-2, 3] + - [60, 2e-1, 3] + - [40, 8e-1, 3] + + # PDF set + pdfset: + name: NNPDF40_nlo_pch_as_01180 + member: -1 #N>=0 for a specific member (0 for central) ; N<0 for all members to be used randomly(flat dist) + +################################# +# Optimiser +################################# +# Parameters of the optimiser managed by ceres-solver +Optimizer: + max_num_iterations: 3000 + chi2_tolerance: 3 + +################################# +# NNAD +################################# +NNAD: + # Initialisation seed + seed: 0 + + # Architecture + architecture: [1, 20, 7] + + # The output function can be either the activation function of the + # NN (0), or linear (1), or quadratic (2). If this entry is absent a + # linear function is assumed. + output function: 2 + + # The flavour map gives the the specific combinations of + # distributions in the physical-basis (d, u, s, ..) to be fitted to + # the data. The number of combinations has to match the number of + # nodes of the output layer of the NN given in the + # architecture. When defining the flavour map, one should keep in + # mind that the code computes predictions using the QCD-evolution + # basis (Sigma, V, T3, ...). Therefore, in general, + # QCD-evolution-like combinations should be preferred. Moreover, the + # distributions are assumed to be for the positive charge of the a + # given hadronic species. The negative distributions are derived + # using charge conjugation (q->qbar). + + # tb bb cb sb ub db g d u s c b t # Combinations to be fitted (i.e. output of the NN): + flavour map: [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, # - db + 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, # - g + 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, # - d = ubar + 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, # - u + 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, # - s+ + 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, # - c+ + 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] # - b+ + combine: true +################################# +# Data +################################# +Data: + # Seed used for the replica generation and the splitting between + # training and validation (do not use a too large number here in + # order to avoid correlation in the replica generation). + seed: 2 + + # Hadronic species being fitted. This is used to make sure that all + # data sets included in the fit actually refer to the correct + # species. + hadron: PI + + # Datasets to be included in the fit along with specific cuts and + # traning fraction. Each single dataset can implement an arbitrary + # number of cuts determined by the name of the appropriate function + # (e.g. zcut) and the allowed range. + sets: + - {name: "COMPASS $pi^+$ 2026", file: "COMPASS_PI_PLUS_2026.yaml", cuts: [{name: Qcut, min: 1.5, max: 50}], training fraction: 0.5} + - {name: "COMPASS $pi^-$ 2026", file: "COMPASS_PI_MINUS_2026.yaml", cuts: [{name: Qcut, min: 1.5, max: 50}], training fraction: 0.5} + - {name: "COMPASS $pi^+$ 2025", file: "COMPASS_PI_PLUS_2025.yaml", cuts: [{name: Qcut, min: 1.5, max: 50}], training fraction: 0.5} + - {name: "COMPASS $pi^-$ 2025", file: "COMPASS_PI_MINUS_2025.yaml", cuts: [{name: Qcut, min: 1.5, max: 50}], training fraction: 0.5} + - {name: "HERMES $\\pi^-$ deuteron", file: "HERMES_PI_MINUS_DEUTERON.yaml", cuts: [{name: Qcut, min: 1.5, max: 50}, {name: zcut, min: 0.2, max: 0.8}], training fraction: 0.5} + - {name: "HERMES $\\pi^-$ proton", file: "HERMES_PI_MINUS_PROTON.yaml", cuts: [{name: Qcut, min: 1.5, max: 50}, {name: zcut, min: 0.2, max: 0.8}], training fraction: 0.5} + - {name: "HERMES $\\pi^+$ deuteron", file: "HERMES_PI_PLUS_DEUTERON.yaml", cuts: [{name: Qcut, min: 1.5, max: 50}, {name: zcut, min: 0.2, max: 0.8}], training fraction: 0.5} + - {name: "HERMES $\\pi^+$ proton", file: "HERMES_PI_PLUS_PROTON.yaml", cuts: [{name: Qcut, min: 1.5, max: 50}, {name: zcut, min: 0.2, max: 0.8}], training fraction: 0.5} + #- {name: "COMPASS $\\pi^-$", file: "COMPASS_PI_MINUS_DECORR.yaml", cuts: [{name: Qcut, min: 2.0, max: 50}], training fraction: 0.5} + #- {name: "COMPASS $\\pi^+$", file: "COMPASS_PI_PLUS_DECORR.yaml", cuts: [{name: Qcut, min: 2.0, max: 50}], training fraction: 0.5} + - {name: "BELLE $\\pi^\\pm$", file: "BELLE_PI_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "BABAR prompt $\\pi^\\pm$", file: "BABAR_PI_PLUS_MINUS_PROMPT.yaml", cuts: [{name: zcut, min: 0.076, max: 0.9}], training fraction: 0.5} + - {name: "TASSO 12 GeV $\\pi^\\pm$", file: "TASSO_12_PI_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "TASSO 14 GeV $\\pi^\\pm$", file: "TASSO_14_PI_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "TASSO 22 GeV $\\pi^\\pm$", file: "TASSO_22_PI_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "TPC $\\pi^\\pm$", file: "TPC_PI_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "TASSO 30 GeV $\\pi^\\pm$", file: "TASSO_30_PI_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "TASSO 34 GeV $\\pi^\\pm$", file: "TASSO_34_PI_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "TASSO 44 GeV $\\pi^\\pm$", file: "TASSO_44_PI_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "TOPAZ $\\pi^\\pm$", file: "TOPAZ_PI_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "ALEPH $\\pi^\\pm$", file: "ALEPH_PI_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "DELPHI total $\\pi^\\pm$", file: "DELPHI_PI_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "DELPHI $uds$ $\\pi^\\pm$", file: "DELPHI_PI_PLUS_MINUS_UDS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "DELPHI bottom $\\pi^\\pm$", file: "DELPHI_PI_PLUS_MINUS_B.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "OPAL $\\pi^\\pm$", file: "OPAL_PI_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "SLD total $\\pi^\\pm$", file: "SLD_PI_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "SLD $uds$ $\\pi^\\pm$", file: "SLD_PI_PLUS_MINUS_UDS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + #- {name: "SLD charm $\\pi^\\pm$", file: "SLD_PI_PLUS_MINUS_C.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "SLD bottom $\\pi^\\pm$", file: "SLD_PI_PLUS_MINUS_B.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} diff --git a/config/HAPS-PiFF1.0-NNLO.yaml b/config/HAPS-PiFF1.0-NNLO.yaml new file mode 100644 index 000000000..521b1c39e --- /dev/null +++ b/config/HAPS-PiFF1.0-NNLO.yaml @@ -0,0 +1,126 @@ +################################# +# Predictions +################################# +Predictions: + # Perturbative order, 0: LO, 1: NLO + perturbative order: 2 + + # Initial scale in GeV to be used for the DGLAP evolution of the FFs. + mu0: 5 + + # Quark thresholds + thresholds: [0, 0, 0, 1.51, 4.92] + + # Strong coupling + alphas: + aref: 0.118 + Qref: 91.1876 + + # Fine-structure constant + alphaem: + aref: 0.00776578395589 + Qref: 91.1876 + + # APFEL++ grid + xgrid: + - [100, 1e-2, 3] + - [60, 2e-1, 3] + - [40, 8e-1, 3] + + # PDF set + pdfset: + name: NNPDF40_nnlo_pch_as_01180 + member: -1 #N>=0 for a specific member (0 for central) ; N<0 for all members to be used randomly(flat dist) + +################################# +# Optimiser +################################# +# Parameters of the optimiser managed by ceres-solver +Optimizer: + max_num_iterations: 3000 + chi2_tolerance: 3 + +################################# +# NNAD +################################# +NNAD: + # Initialisation seed + seed: 0 + + # Architecture + architecture: [1, 20, 7] + + # The output function can be either the activation function of the + # NN (0), or linear (1), or quadratic (2). If this entry is absent a + # linear function is assumed. + output function: 2 + + # The flavour map gives the the specific combinations of + # distributions in the physical-basis (d, u, s, ..) to be fitted to + # the data. The number of combinations has to match the number of + # nodes of the output layer of the NN given in the + # architecture. When defining the flavour map, one should keep in + # mind that the code computes predictions using the QCD-evolution + # basis (Sigma, V, T3, ...). Therefore, in general, + # QCD-evolution-like combinations should be preferred. Moreover, the + # distributions are assumed to be for the positive charge of the a + # given hadronic species. The negative distributions are derived + # using charge conjugation (q->qbar). + + # tb bb cb sb ub db g d u s c b t # Combinations to be fitted (i.e. output of the NN): + flavour map: [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, # - db + 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, # - g + 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, # - d = ubar + 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, # - u + 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, # - s+ + 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, # - c+ + 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0] # - b+ + combine: true +################################# +# Data +################################# +Data: + # Seed used for the replica generation and the splitting between + # training and validation (do not use a too large number here in + # order to avoid correlation in the replica generation). + seed: 2 + + # Hadronic species being fitted. This is used to make sure that all + # data sets included in the fit actually refer to the correct + # species. + hadron: PI + + # Datasets to be included in the fit along with specific cuts and + # traning fraction. Each single dataset can implement an arbitrary + # number of cuts determined by the name of the appropriate function + # (e.g. zcut) and the allowed range. + sets: + - {name: "COMPASS $pi^+$ 2026", file: "COMPASS_PI_PLUS_2026.yaml", cuts: [{name: Qcut, min: 1.5, max: 50}], training fraction: 0.5} + - {name: "COMPASS $pi^-$ 2026", file: "COMPASS_PI_MINUS_2026.yaml", cuts: [{name: Qcut, min: 1.5, max: 50}], training fraction: 0.5} + - {name: "COMPASS $pi^+$ 2025", file: "COMPASS_PI_PLUS_2025.yaml", cuts: [{name: Qcut, min: 1.5, max: 50}], training fraction: 0.5} + - {name: "COMPASS $pi^-$ 2025", file: "COMPASS_PI_MINUS_2025.yaml", cuts: [{name: Qcut, min: 1.5, max: 50}], training fraction: 0.5} + - {name: "HERMES $\\pi^-$ deuteron", file: "HERMES_PI_MINUS_DEUTERON.yaml", cuts: [{name: Qcut, min: 1.5, max: 50}, {name: zcut, min: 0.2, max: 0.8}], training fraction: 0.5} + - {name: "HERMES $\\pi^-$ proton", file: "HERMES_PI_MINUS_PROTON.yaml", cuts: [{name: Qcut, min: 1.5, max: 50}, {name: zcut, min: 0.2, max: 0.8}], training fraction: 0.5} + - {name: "HERMES $\\pi^+$ deuteron", file: "HERMES_PI_PLUS_DEUTERON.yaml", cuts: [{name: Qcut, min: 1.5, max: 50}, {name: zcut, min: 0.2, max: 0.8}], training fraction: 0.5} + - {name: "HERMES $\\pi^+$ proton", file: "HERMES_PI_PLUS_PROTON.yaml", cuts: [{name: Qcut, min: 1.5, max: 50}, {name: zcut, min: 0.2, max: 0.8}], training fraction: 0.5} + #- {name: "COMPASS $\\pi^-$", file: "COMPASS_PI_MINUS_DECORR.yaml", cuts: [{name: Qcut, min: 2.0, max: 50}], training fraction: 0.5} + #- {name: "COMPASS $\\pi^+$", file: "COMPASS_PI_PLUS_DECORR.yaml", cuts: [{name: Qcut, min: 2.0, max: 50}], training fraction: 0.5} + - {name: "BELLE $\\pi^\\pm$", file: "BELLE_PI_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "BABAR prompt $\\pi^\\pm$", file: "BABAR_PI_PLUS_MINUS_PROMPT.yaml", cuts: [{name: zcut, min: 0.076, max: 0.9}], training fraction: 0.5} + - {name: "TASSO 12 GeV $\\pi^\\pm$", file: "TASSO_12_PI_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "TASSO 14 GeV $\\pi^\\pm$", file: "TASSO_14_PI_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "TASSO 22 GeV $\\pi^\\pm$", file: "TASSO_22_PI_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "TPC $\\pi^\\pm$", file: "TPC_PI_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "TASSO 30 GeV $\\pi^\\pm$", file: "TASSO_30_PI_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "TASSO 34 GeV $\\pi^\\pm$", file: "TASSO_34_PI_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "TASSO 44 GeV $\\pi^\\pm$", file: "TASSO_44_PI_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "TOPAZ $\\pi^\\pm$", file: "TOPAZ_PI_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.075, max: 0.9}], training fraction: 0.5} + - {name: "ALEPH $\\pi^\\pm$", file: "ALEPH_PI_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "DELPHI total $\\pi^\\pm$", file: "DELPHI_PI_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "DELPHI $uds$ $\\pi^\\pm$", file: "DELPHI_PI_PLUS_MINUS_UDS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "DELPHI bottom $\\pi^\\pm$", file: "DELPHI_PI_PLUS_MINUS_B.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "OPAL $\\pi^\\pm$", file: "OPAL_PI_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "SLD total $\\pi^\\pm$", file: "SLD_PI_PLUS_MINUS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "SLD $uds$ $\\pi^\\pm$", file: "SLD_PI_PLUS_MINUS_UDS.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + #- {name: "SLD charm $\\pi^\\pm$", file: "SLD_PI_PLUS_MINUS_C.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} + - {name: "SLD bottom $\\pi^\\pm$", file: "SLD_PI_PLUS_MINUS_B.yaml", cuts: [{name: zcut, min: 0.02, max: 0.9}], training fraction: 0.5} From bc7b2a5139860fa0698cbd3d66d53da01f1ee6df Mon Sep 17 00:00:00 2001 From: Maryam Soleymaninia <147120752+Maryamgithub63@users.noreply.github.com> Date: Tue, 16 Jun 2026 15:47:28 +0400 Subject: [PATCH 4/4] Add HAPS COMPASS SIDIS data files --- data/COMPASS_KA_MINUS_2025.yaml | 2352 +++++++++++++++++++++++++++++ data/COMPASS_KA_MINUS_2026.yaml | 2464 ++++++++++++++++++++++++++++++ data/COMPASS_KA_PLUS_2025.yaml | 2408 ++++++++++++++++++++++++++++++ data/COMPASS_KA_PLUS_2026.yaml | 2464 ++++++++++++++++++++++++++++++ data/COMPASS_PI_MINUS_2025.yaml | 2440 ++++++++++++++++++++++++++++++ data/COMPASS_PI_MINUS_2026.yaml | 2472 +++++++++++++++++++++++++++++++ data/COMPASS_PI_PLUS_2025.yaml | 2440 ++++++++++++++++++++++++++++++ data/COMPASS_PI_PLUS_2026.yaml | 2472 +++++++++++++++++++++++++++++++ 8 files changed, 19512 insertions(+) create mode 100644 data/COMPASS_KA_MINUS_2025.yaml create mode 100644 data/COMPASS_KA_MINUS_2026.yaml create mode 100644 data/COMPASS_KA_PLUS_2025.yaml create mode 100644 data/COMPASS_KA_PLUS_2026.yaml create mode 100644 data/COMPASS_PI_MINUS_2025.yaml create mode 100644 data/COMPASS_PI_MINUS_2026.yaml create mode 100644 data/COMPASS_PI_PLUS_2025.yaml create mode 100644 data/COMPASS_PI_PLUS_2026.yaml diff --git a/data/COMPASS_KA_MINUS_2025.yaml b/data/COMPASS_KA_MINUS_2025.yaml new file mode 100644 index 000000000..d2931651a --- /dev/null +++ b/data/COMPASS_KA_MINUS_2025.yaml @@ -0,0 +1,2352 @@ +dependent_variables: + - header: {title: COMPASS $K^-$ Multiplicities} + qualifiers: + - {name: process, value: SIDIS} + - {name: observable, value: dsigma/dxdydz} + - {name: target_isoscalarity, value: 1.0} + - {name: prefactor, value: 1} + - {name: Vs, value: 17.35} + - {name: Q, low: 0.9, high: 8.0, integrate: true} + - {name: x, low: 0.004, high: 0.4, integrate: true} + - {name: z, low: 0.2, high: 0.85, integrate: true} + - {name: PS_reduction, W: 5, ymin: 0.1, ymax: 0.7} + - {name: hadron, value: KA} + - {name: charge, value: -1} + values: + - errors: + - {label: unc, value: 0.0147} + - {label: add, value: 0.04683} + value: 0.32837 + - errors: + - {label: unc, value: 0.01152} + - {label: add, value: 0.04661} + value: 0.24748 + - errors: + - {label: unc, value: 0.00898} + - {label: add, value: 0.04681} + value: 0.17844 + - errors: + - {label: unc, value: 0.00763} + - {label: add, value: 0.04766} + value: 0.13949 + - errors: + - {label: unc, value: 0.00615} + - {label: add, value: 0.05105} + value: 0.10077 + - errors: + - {label: unc, value: 0.00553} + - {label: add, value: 0.0551} + value: 0.08276 + - errors: + - {label: unc, value: 0.00513} + - {label: add, value: 0.0597} + value: 0.0595 + - errors: + - {label: unc, value: 0.00521} + - {label: add, value: 0.07156} + value: 0.0417 + - errors: + - {label: unc, value: 0.0148} + - {label: add, value: 0.05} + value: 0.3504 + - errors: + - {label: unc, value: 0.01146} + - {label: add, value: 0.05019} + value: 0.2592 + - errors: + - {label: unc, value: 0.00909} + - {label: add, value: 0.05077} + value: 0.19366 + - errors: + - {label: unc, value: 0.008} + - {label: add, value: 0.05078} + value: 0.14936 + - errors: + - {label: unc, value: 0.00966} + - {label: add, value: 0.05422} + value: 0.13442 + - errors: + - {label: unc, value: 0.01213} + - {label: add, value: 0.04822} + value: 0.09208 + - errors: + - {label: unc, value: 0.00705} + - {label: add, value: 0.05474} + value: 0.06182 + - errors: + - {label: unc, value: 0.00629} + - {label: add, value: 0.05856} + value: 0.05137 + - errors: + - {label: unc, value: 0.00549} + - {label: add, value: 0.07859} + value: 0.03746 + - errors: + - {label: unc, value: 0.00436} + - {label: add, value: 0.06407} + value: 0.02697 + - errors: + - {label: unc, value: 0.0043} + - {label: add, value: 0.04638} + value: 0.02277 + - errors: + - {label: unc, value: 0.00539} + - {label: add, value: 0.04608} + value: 0.02795 + - errors: + - {label: unc, value: 0.00159} + - {label: add, value: 0.05623} + value: 0.00626 + - errors: + - {label: unc, value: 0.00768} + - {label: add, value: 0.04402} + value: 0.16464 + - errors: + - {label: unc, value: 0.00557} + - {label: add, value: 0.04518} + value: 0.12502 + - errors: + - {label: unc, value: 0.0043} + - {label: add, value: 0.04776} + value: 0.09078 + - errors: + - {label: unc, value: 0.00377} + - {label: add, value: 0.05019} + value: 0.0746 + - errors: + - {label: unc, value: 0.00296} + - {label: add, value: 0.05514} + value: 0.05165 + - errors: + - {label: unc, value: 0.00249} + - {label: add, value: 0.06229} + value: 0.03866 + - errors: + - {label: unc, value: 0.00195} + - {label: add, value: 0.05537} + value: 0.02875 + - errors: + - {label: unc, value: 0.00159} + - {label: add, value: 0.04686} + value: 0.02168 + - errors: + - {label: unc, value: 0.00146} + - {label: add, value: 0.04719} + value: 0.01763 + - errors: + - {label: unc, value: 0.0009} + - {label: add, value: 0.04749} + value: 0.01196 + - errors: + - {label: unc, value: 0.01273} + - {label: add, value: 0.04468} + value: 0.32965 + - errors: + - {label: unc, value: 0.00876} + - {label: add, value: 0.04461} + value: 0.23562 + - errors: + - {label: unc, value: 0.00697} + - {label: add, value: 0.04477} + value: 0.18281 + - errors: + - {label: unc, value: 0.00551} + - {label: add, value: 0.04516} + value: 0.1387 + - errors: + - {label: unc, value: 0.00449} + - {label: add, value: 0.0472} + value: 0.10577 + - errors: + - {label: unc, value: 0.00347} + - {label: add, value: 0.04864} + value: 0.07599 + - errors: + - {label: unc, value: 0.00295} + - {label: add, value: 0.05227} + value: 0.05755 + - 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header: {title: COMPASS $pi^+$ Multiplicities} + qualifiers: + - {name: process, value: SIDIS} + - {name: observable, value: dsigma/dxdydz} + - {name: target_isoscalarity, value: 1.0} + - {name: prefactor, value: 1} + - {name: Vs, value: 17.35} + - {name: Q, low: 0.9, high: 8.0, integrate: true} + - {name: x, low: 0.004, high: 0.4, integrate: true} + - {name: z, low: 0.2, high: 0.85, integrate: true} + - {name: PS_reduction, W: 5, ymin: 0.1, ymax: 0.7} + - {name: hadron, value: PI} + - {name: charge, value: 1} + values: + - errors: + - {label: unc, value: 0.07824} + - {label: add, value: 0.04636} + value: 2.15047 + - errors: + - {label: unc, value: 0.0549} + - {label: add, value: 0.04644} + value: 1.48649 + - errors: + - {label: unc, value: 0.03995} + - {label: add, value: 0.04681} + value: 1.0496 + - errors: + - {label: unc, value: 0.02936} + - {label: add, value: 0.04732} + value: 0.74042 + - errors: + - {label: unc, value: 0.02318} + - {label: add, value: 0.0481} + value: 0.55972 + - 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