A framework based on linear response and influence functions maps data sensitivities in global QCD analyses to show how experiments determine central values, uncertainties, and correlations of non-perturbative functions.
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Determination of the theory uncertainties from missing higher orders on NNLO parton distributions with percent accuracy
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Presents a linear PDF parametrization from dimensionality-reduced neural network bases for efficient Bayesian inference, tested via multi-closure tests on synthetic deep inelastic scattering data.
TQ4Q2.0 supplies the first complete, uncertainty-quantified set of NRQCD-based fragmentation functions for all-heavy tetraquarks, including nonconstituent channels and public grids for jet-associated production.
The OMG3Q1.1 framework delivers the first uncertainty-quantified set of fragmentation functions for all-heavy Ω_{3Q} baryons via diquark-inspired inputs, HF-NRevo evolution, and replica-based error estimation.
A neural network trained solely on integral observables from a known GPD model recovers the main features of the underlying distributions in a closure test.
A multimodal, uncertainty-quantified set of leading-power fragmentation functions for all-charm pentaquarks is constructed and applied to NLL/NLO+ pentaquark-plus-jet production at future hadron colliders.
citing papers explorer
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Mapping data sensitivities in global QCD analysis with linear response and influence functions
A framework based on linear response and influence functions maps data sensitivities in global QCD analyses to show how experiments determine central values, uncertainties, and correlations of non-perturbative functions.
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A linear PDF model for Bayesian inference
Presents a linear PDF parametrization from dimensionality-reduced neural network bases for efficient Bayesian inference, tested via multi-closure tests on synthetic deep inelastic scattering data.
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All-charm tetraquarks at hadron colliders: A high-precision fragmentation perspective
TQ4Q2.0 supplies the first complete, uncertainty-quantified set of NRQCD-based fragmentation functions for all-heavy tetraquarks, including nonconstituent channels and public grids for jet-associated production.
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Toward Precision Fragmentation of $\Omega_{3Q}$ Baryons: The OMG3Q1.1 Framework
The OMG3Q1.1 framework delivers the first uncertainty-quantified set of fragmentation functions for all-heavy Ω_{3Q} baryons via diquark-inspired inputs, HF-NRevo evolution, and replica-based error estimation.
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Neural Network Representation of Generalized Parton Distributions (NNGPD)
A neural network trained solely on integral observables from a known GPD model recovers the main features of the underlying distributions in a closure test.
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Multimodal Fragmentation of All-Heavy Pentaquarks: Uncertainty-Aware Predictions for Hadron Colliders
A multimodal, uncertainty-quantified set of leading-power fragmentation functions for all-charm pentaquarks is constructed and applied to NLL/NLO+ pentaquark-plus-jet production at future hadron colliders.