%0 Journal Article
%T Nucleon axial form factor from a Bayesian neural-network analysis of neutrino-scattering data
%A Alvarez-Ruso, L.
%A Graczyk, K. M.
%A Saul-Sala, E.
%J Physical Review C
%D 2019
%V 99
%N 2
%I Amer Physical Soc
%@ 2469-9985
%G English
%F Alvarez-Ruso_etal2019
%O WOS:000459206200011
%O exported from refbase (https://references.ific.uv.es/refbase/show.php?record=3915), last updated on Sun, 03 Mar 2019 18:13:41 +0000
%X 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-GeV2 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.
%R 10.1103/PhysRevC.99.025204
%U https://arxiv.org/abs/1805.00905
%U https://doi.org/10.1103/PhysRevC.99.025204
%P 025204-14pp