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.
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.
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.
The authors construct and publicly release the TQ4Q2.0 fragmentation functions for all-heavy S-wave tetraquarks via NRQCD factorization, extending prior work with nonconstituent contributions and replica-based uncertainties.
Develops uncertainty-aware fragmentation functions PQ5Q1.1 for all-charm pentaquarks using multimodal perturbative and nonperturbative modeling for collider predictions.
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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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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All-charm tetraquarks at hadron colliders: A high-precision fragmentation perspective
The authors construct and publicly release the TQ4Q2.0 fragmentation functions for all-heavy S-wave tetraquarks via NRQCD factorization, extending prior work with nonconstituent contributions and replica-based uncertainties.
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Multimodal Fragmentation of All-Heavy Pentaquarks: Uncertainty-Aware Predictions for Hadron Colliders
Develops uncertainty-aware fragmentation functions PQ5Q1.1 for all-charm pentaquarks using multimodal perturbative and nonperturbative modeling for collider predictions.