Nucleon axial form factor from a Bayesian neural-network analysis of neutrino-scattering data

TL;DR

This paper employs Bayesian neural networks to extract the nucleon axial form factor from neutrino-deuteron scattering data, revealing sensitivities to low-Q^2 data and deuteron corrections, and highlighting the need for more precise measurements.

Contribution

It introduces a model-independent Bayesian neural network approach to determine the nucleon axial form factor from neutrino scattering data.

Findings

Disagreement in axial radius when including low-Q^2 data.

High sensitivity to deuteron structure corrections.

No significant deviation from the dipole model without low-Q^2 data.

Abstract

The Bayesian approach for feed-forward neural networks has been applied to the extraction of the nucleon axial form factor from the neutrino-deuteron scattering data measured by the Argonne National Laboratory (ANL) bubble chamber experiment. This framework allows to perform a model-independent determination of the axial form factor from data.. When the low $0.05 < Q^2 < 0.10$ GeV$^2$ data is included in the analysis, the resulting axial radius disagrees with available determinations. Furthermore, a large sensitivity to the corrections from the deuteron structure is obtained. In turn, when the low-$Q^2$ region is not taken into account, with or without deuteron corrections, no significant deviations from the dipole ansatz have been observed. A more accurate determination of the nucleon axial form factor requires new precise measurements of neutrino-induced quasielastic scattering on hydrogen and deuterium.