TL;DR
This paper introduces a Bayesian non-parametric approach using constrained Gaussian processes to estimate the proton radius from scattering data, aiming to address the longstanding proton-radius puzzle.
Contribution
It presents a novel, flexible non-parametric method that incorporates physical constraints to model the proton's electric form factor without parametric assumptions.
Findings
Results depend on data range and constraints used.
Low momentum data with normalization constraint aligns with muonic results.
Shape constraint favors larger electronic measurements.
Abstract
Background: The "proton radius puzzle" refers to an eight-year old problem that highlights major inconsistencies in the extraction of the charge radius of the proton from muonic Lamb-shift experiments as compared against experiments using elastic electron scattering. For the latter, the determination of the charge radius involves an extrapolation of the experimental form factor to zero momentum transfer. Purpose: To estimate the proton radius by introducing a novel non-parametric approach to model the electric form factor of the proton. Methods: Within a Bayesian paradigm, we develop a model flexible enough to fit the data without any parametric assumptions on the form factor. The Bayesian estimation is guided by imposing only two physical constraints on the form factor: (a) its value at zero momentum transfer (normalization) and (b) its overall shape, assumed to be a monotonically…
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