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A simple non-parametric reconstruction of parton distributions from limited Fourier information
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Some calculations of parton distributions from first principles only give access to a limited range of Fourier modes of the function to reconstruct. We present a physically motivated procedure to regularize the inverse integral problem using a Gaussian process as a Bayesian prior. We propose to fix the hyperparameters of the prior in a meaningful physical fashion, offering a simple implementation, great numerical efficiency, and allowing us to understand and keep control easily of the uncertainty of the reconstruction.
Forward citations
Cited by 3 Pith papers
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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 d...
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Gradient flow for parton distribution functions: first application to the pion
Pion PDF moment ratios up to <x^5> were extracted from lattice QCD with gradient flow and agree with phenomenological fits.
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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 ...
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