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Add EIC pseudodata cards used in arXiv:2602.17298 #1

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e38bc9c
Add HAPS EIC polarized PDF fit cards
hamzeh-khanpour Jun 3, 2026
81f6ec8
Add HAPS EIC SIDIS pseudodata files
hamzeh-khanpour Jun 3, 2026
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168 changes: 168 additions & 0 deletions config/Polarized_PDFs_NLO_W2cut_4_EIC_18x275_Z_0-1.yaml
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#################################
# Predictions
#################################
Predictions:
# Perturbative order, 0: LO, 1: NLO 2: NNLO
perturbative order: 1

# Initial scale in GeV to be used for the DGLAP evolution of the FFs.
mu0: 1

# Quark thresholds
thresholds: [0, 0, 0, 1.51, 4.92]

# Strong coupling
alphas:
aref: 0.1180
Qref: 91.1876

# APFEL++ grid
xgrid:
- [200, 1e-5, 3]
- [100, 1e-1, 3]
- [80, 8e-1, 3]

###################
# Replica settings
###################
#
# Here you can choose how replicas in the unpolarised PDF and FF
# sets are used in the analysis.
# 0: The central replica (mean value) is chosen for both
# unpolarised PDF and FF sets, overwriting the values
# provided below.
# -1: PDF and FF replicas are chosen randomly, overwriting
# the values provided below.
# 1: The values provided below are used to select a particular
# replica in the set.
# If the fit is performed with unfluctuated data ("./Optimise 0"), option 0
# is automatically adopted.
Replica settings: -1

##############
# Positivity
##############
# Here you can select how the positivity constraint is
# implemented.
# false: the positivity bound is imposed with a unpolarised
# PDF replica selected randomly from the set.
# true: the positivity bound is imposed through the central
# replica of the unpolarised PDF set inflated by a
# factor specified in "Multiplicative factor".
Include Std: false
#Multiplicative factor: 5

# List of external sets to be used in the analysis.
# name: is the number of the PDF set as provided in
# the LHAPDF list.
# member: replica member to be used in the analysis.
# This key is overwritten if 'Replica settings'
# is either set to 0 or -1.
Sets:
# Unpolarised PDF set
unpolarised pdfset:
name: NNPDF40_nlo_pch_as_01180
member: -1 #N>=0 for a specific member (0 for central)

# FF sets
PIplus ffset:
name: MAPFF10NLOPIp
member: -1

PIminus ffset:
name: MAPFF10NLOPIm
member: -1

KAplus ffset:
name: MAPFF10NLOKAp
member: -1

KAminus ffset:
name: MAPFF10NLOKAm
member: -1

#################################
# Optimiser
#################################
# Parameters of the optimiser managed by ceres-solver
Optimizer:
max_num_iterations: 3000
chi2_tolerance: 3

#################################
# NNAD
#################################
NNAD:
# Initialisation seed
seed: 0

# Flavour map
# Available flavour maps:
# - "separate sbar" (0): sbar, ubar, dbar, g, d, u, s
# - "s=sbar" (1): ubar, dbar, g, u, d, s = sbar.
flavour map: 0

# Architecture
# DO NOT MODIFY THE INPUT
# The output must be consistent with the flavour map:
# - "separate sbar" : 7
# - "s=sbar" : 6
architecture: [1, 10, 7]

#################################
# 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

# Minimum number of points below which all points will be included in the training.
# If not provided, Denali sets this number to 10.
minimum training size: 10

# 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. xcut) and the allowed range.
sets:
- {name: "EIC $KA^-$ A1p EIC 18x275", file: "asymunf_18x275_kaonsneg_after_smear.yaml", cuts: [{name: Qcut, min: 1, max: 100}, {name: zcut, min: 0.1, max: 0.99}, {name: xcut, min: 0.00001, max: 0.99}], training fraction: 0.8}
- {name: "EIC $KA^+$ A1p EIC 18x275", file: "asymunf_18x275_kaonspos_after_smear.yaml", cuts: [{name: Qcut, min: 1, max: 100}, {name: zcut, min: 0.1, max: 0.99}, {name: xcut, min: 0.00001, max: 0.99}], training fraction: 0.8}
- {name: "EIC $PI^-$ A1p EIC 18x275", file: "asymunf_18x275_pionsneg_after_smear.yaml", cuts: [{name: Qcut, min: 1, max: 100}, {name: zcut, min: 0.1, max: 0.99}, {name: xcut, min: 0.00001, max: 0.99}], training fraction: 0.8}
- {name: "EIC $PI^+$ A1p EIC 18x275", file: "asymunf_18x275_pionspos_after_smear.yaml", cuts: [{name: Qcut, min: 1, max: 100}, {name: zcut, min: 0.1, max: 0.99}, {name: xcut, min: 0.00001, max: 0.99}], training fraction: 0.8}
- {name: "COMPASS KA^- A_1^p", file: "COMPASS_A1P_KA_MINUS.yaml", cuts: [{name: Qcut, min: 1, max: 100}], training fraction: 0.8}
- {name: "COMPASS KA^+ A_1^p", file: "COMPASS_A1P_KA_PLUS.yaml", cuts: [{name: Qcut, min: 1, max: 100}], training fraction: 0.8}
- {name: "COMPASS PI^- A_1^p", file: "COMPASS_A1P_PI_MINUS.yaml", cuts: [{name: Qcut, min: 1, max: 100}], training fraction: 0.8}
- {name: "COMPASS PI^+ A_1^p", file: "COMPASS_A1P_PI_PLUS.yaml", cuts: [{name: Qcut, min: 1, max: 100}], training fraction: 0.8}
- {name: "COMPASS KA^- A_1^d", file: "COMPASS_A1D_KA_MINUS.yaml", cuts: [{name: Qcut, min: 1, max: 100}], training fraction: 0.8}
- {name: "COMPASS KA^+ A_1^d", file: "COMPASS_A1D_KA_PLUS.yaml", cuts: [{name: Qcut, min: 1, max: 100}], training fraction: 0.8}
- {name: "COMPASS PI^- A_1^d", file: "COMPASS_A1D_PI_MINUS.yaml", cuts: [{name: Qcut, min: 1, max: 100}], training fraction: 0.8}
- {name: "COMPASS PI^+ A_1^d", file: "COMPASS_A1D_PI_PLUS.yaml", cuts: [{name: Qcut, min: 1, max: 100}], training fraction: 0.8}
- {name: "HERMES 2018 KA^- A_1^d", file: "HERMES_2018_A1D_KA_MINUS.yaml", cuts: [{name: Qcut, min: 1, max: 100}], training fraction: 0.8}
- {name: "HERMES 2018 KA^+ A_1^d", file: "HERMES_2018_A1D_KA_PLUS.yaml", cuts: [{name: Qcut, min: 1, max: 100}], training fraction: 0.8}
- {name: "HERMES 2018 PI^- A_1^d", file: "HERMES_2018_A1D_PI_MINUS.yaml", cuts: [{name: Qcut, min: 1, max: 100}], training fraction: 0.8}
- {name: "HERMES 2018 PI^+ A_1^d", file: "HERMES_2018_A1D_PI_PLUS.yaml", cuts: [{name: Qcut, min: 1, max: 100}], training fraction: 0.8}
- {name: "HERMES 2018 PI^- A_1^p", file: "HERMES_2018_A1P_PI_MINUS.yaml", cuts: [{name: Qcut, min: 1, max: 100}], training fraction: 0.8}
- {name: "HERMES 2018 PI^+ A_1^p", file: "HERMES_2018_A1P_PI_PLUS.yaml", cuts: [{name: Qcut, min: 1, max: 100}], training fraction: 0.8}
- {name: "E142 g_1^N", file: "E142_G1N.yaml", cuts: [{name: Wcut, min: 2.0, max: 100}, {name: Qcut, min: 1, max: 100}], training fraction: 0.8}
- {name: "E143 g_1^D", file: "E143_G1D.yaml", cuts: [{name: Wcut, min: 2.0, max: 100}, {name: Qcut, min: 1, max: 100}], training fraction: 0.8}
- {name: "E143 g_1^P", file: "E143_G1P.yaml", cuts: [{name: Wcut, min: 2.0, max: 100}, {name: Qcut, min: 1, max: 100}], training fraction: 0.8}
- {name: "E154 g_1^N", file: "E154_G1N.yaml", cuts: [{name: Wcut, min: 2.0, max: 100}, {name: Qcut, min: 1, max: 100}], training fraction: 0.8}
- {name: "E155 g_1^P/F_1^P", file: "E155_G1P_F1P.yaml", cuts: [{name: Wcut, min: 2.0, max: 100}, {name: Qcut, min: 1, max: 100}], training fraction: 0.8}
- {name: "E155 g_1^N/F_1^N", file: "E155_G1N_F1N.yaml", cuts: [{name: Wcut, min: 2.0, max: 100}, {name: Qcut, min: 1, max: 100}], training fraction: 0.8}
- {name: "EMC g_1^P", file: "EMC_G1P.yaml", cuts: [{name: Wcut, min: 2.0, max: 100}, {name: Qcut, min: 1, max: 100}], training fraction: 0.8}
- {name: "JLAB E06 014 g_1^N/F_1^N", file: "JLAB_E06_014_G1N_F1N.yaml", cuts: [{name: Wcut, min: 2.0, max: 100}, {name: Qcut, min: 1, max: 100}], training fraction: 0.8}
- {name: "JLAB E97 103 g_1^N", file: "JLAB_E97_103_G1N.yaml", cuts: [{name: Wcut, min: 2.0, max: 100}, {name: Qcut, min: 1, max: 100}], training fraction: 0.8}
- {name: "JLAB E99 117 g_1^N/F_1^N", file: "JLAB_E99_117_G1N_F1N.yaml", cuts: [{name: Wcut, min: 2.0, max: 100}, {name: Qcut, min: 1, max: 100}], training fraction: 0.8}
- {name: "JLAB EG1 DVCS G_1^D/F_1^D", file: "JLAB_EG1_DVCS_G1D_F1D.yaml", cuts: [{name: Wcut, min: 2.0, max: 100}, {name: Qcut, min: 1, max: 100}], training fraction: 0.8}
- {name: "JLAB EG1 DVCS G_1^P/F_1^P", file: "JLAB_EG1_DVCS_G1P_F1P.yaml", cuts: [{name: Wcut, min: 2.0, max: 100}, {name: Qcut, min: 1, max: 100}], training fraction: 0.8}
- {name: "SMC g_1^D", file: "SMC_G1D.yaml", cuts: [{name: Wcut, min: 2.0, max: 100}, {name: Qcut, min: 1, max: 100}], training fraction: 0.8}
- {name: "SMC g_1^P", file: "SMC_G1P.yaml", cuts: [{name: Wcut, min: 2.0, max: 100}, {name: Qcut, min: 1, max: 100}], training fraction: 0.8}
- {name: "COMPASS g_1^D", file: "COMPASS_G1D.yaml", cuts: [{name: Wcut, min: 2.0, max: 100}, {name: Qcut, min: 1, max: 100}], training fraction: 0.8}
- {name: "COMPASS g_1^P", file: "COMPASS_G1P.yaml", cuts: [{name: Wcut, min: 2.0, max: 100}, {name: Qcut, min: 1, max: 100}], training fraction: 0.8}
- {name: "HERMES g_1^N", file: "HERMES_G1N.yaml", cuts: [{name: Wcut, min: 2.0, max: 100}, {name: Qcut, min: 1, max: 100}], training fraction: 0.8}
- {name: "HERMES g_1^D", file: "HERMES_G1D.yaml", cuts: [{name: Wcut, min: 2.0, max: 100}, {name: Qcut, min: 1, max: 100}], training fraction: 0.8}
- {name: "HERMES g_1^P", file: "HERMES_G1P.yaml", cuts: [{name: Wcut, min: 2.0, max: 100}, {name: Qcut, min: 1, max: 100}], training fraction: 0.8}
# - {name: "a3 sum rule", file: "sum_rule_a3.yaml", cuts: [], training fraction: 1}
# - {name: "a8 sum rule", file: "sum_rule_a8.yaml", cuts: [], training fraction: 1}
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