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<Q2<0.10GeV2 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-Q2 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)
Published by: APS
DOI: 10.1103/PhysRevC.99.025204
arXiv: 1805.00905
License: CC-BY-4.0



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