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

Alvarez-Ruso, Luis (Departamento de Física Teórica and Instituto de Física Corpuscular (IFIC), Centro Mixto UVEG-CSIC, Valencia, Spain) ; Graczyk, Krzysztof M. (Institute of Theoretical Physics, University of Wrocław, plac M. Borna 9, 50-204, Wrocław, Poland) ; Saul-Sala, Eduardo (Departamento de Física Teórica and Instituto de Física Corpuscular (IFIC), Centro Mixto UVEG-CSIC, Valencia, Spain)

19 February 2019

Abstract: The Bayesian approach for feedforward 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 bubble-chamber experiment. This framework allows to perform a model-independent determination of the axial form factor from data. When the low $0.05<{Q}^{2}<0.10-{\mathrm{GeV}}^{2}$ data are 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 previous determinations 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.

Published in: Physical Review C 99 (2019)