@Article{Alvarez-Ruso_etal2019,
author="Alvarez-Ruso, L.
and Graczyk, K. M.
and Saul-Sala, E.",
title="Nucleon axial form factor from a Bayesian neural-network analysis of neutrino-scattering data",
journal="Physical Review C",
year="2019",
publisher="Amer Physical Soc",
volume="99",
number="2",
pages="025204--14pp",
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-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.",
optnote="WOS:000459206200011",
optnote="exported from refbase (https://references.ific.uv.es/refbase/show.php?record=3915), last updated on Sun, 03 Mar 2019 18:13:41 +0000",
issn="2469-9985",
doi="10.1103/PhysRevC.99.025204",
opturl="https://arxiv.org/abs/1805.00905",
opturl="https://doi.org/10.1103/PhysRevC.99.025204",
language="English"
}