Lattice QCD pseudo-distributions at m_π=358 MeV are inverted via multidimensional Gaussian process regression to reconstruct the full kinematic dependence of GPDs H^{u-d} and E^{u-d} while directly extracting double distributions.
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Derives that the MC replica method produces a distribution differing from the Bayesian Laplace approximation by a single computable matrix (residual-weighted Hessian), whose sign and magnitude determine over- or under-estimation of uncertainties in nonlinear models.
The causal bootstrap computes rigorous bounds on smeared spectral functions from non-perturbative Euclidean data by optimizing over the convex set of compatible positive spectral densities and reducing dual problems to semidefinite programs for certain kernels.
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.
Presents a new ensemble regression hyperoptimisation procedure for PDF fitting that combines multiple statistically equivalent methodologies into one PDF set accounting for hyperparameter variation.
An AI-assisted Bayesian framework extracts TMD PDFs from global Drell-Yan data using surrogate models for scalable MCMC sampling.
Framework for exact and approximate kernel transformations between smeared spectral functions, including systematic error bounds computable from input data.
Analysis of non-perturbative lattice data shows that the inverse problem in LaMET introduces significant uncertainties in parton distributions, especially from harmonics around λ=5-15, and that exact asymptotic decay matters little in the currently applicable x range.
citing papers explorer
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Reconstructing the full kinematic dependence of GPDs from pseudo-distributions
Lattice QCD pseudo-distributions at m_π=358 MeV are inverted via multidimensional Gaussian process regression to reconstruct the full kinematic dependence of GPDs H^{u-d} and E^{u-d} while directly extracting double distributions.
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Propagating data noise through the fit: the Monte Carlo replica distribution
Derives that the MC replica method produces a distribution differing from the Bayesian Laplace approximation by a single computable matrix (residual-weighted Hessian), whose sign and magnitude determine over- or under-estimation of uncertainties in nonlinear models.
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The Causal Bootstrap: Bounding Smeared Spectral Functions from Non-Perturbative Euclidean Data
The causal bootstrap computes rigorous bounds on smeared spectral functions from non-perturbative Euclidean data by optimizing over the convex set of compatible positive spectral densities and reducing dual problems to semidefinite programs for certain kernels.
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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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Hyperoptimisation algorithm for the next generation of PDF determinations: ensemble regression with an unbiased selection model
Presents a new ensemble regression hyperoptimisation procedure for PDF fitting that combines multiple statistically equivalent methodologies into one PDF set accounting for hyperparameter variation.
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AI-assisted modeling and Bayesian inference of unpolarized quark transverse momentum distributions from Drell-Yan data
An AI-assisted Bayesian framework extracts TMD PDFs from global Drell-Yan data using surrogate models for scalable MCMC sampling.
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Kernel transformations and bounds for smeared spectral functions
Framework for exact and approximate kernel transformations between smeared spectral functions, including systematic error bounds computable from input data.
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Inverse problem in the LaMET framework
Analysis of non-perturbative lattice data shows that the inverse problem in LaMET introduces significant uncertainties in parton distributions, especially from harmonics around λ=5-15, and that exact asymptotic decay matters little in the currently applicable x range.