TY  - JOUR
AU  - Alvarez-Ruso, L.
AU  - Graczyk, K. M.
AU  - Saul-Sala, E.
PY  - 2019
DA  - 2019//
TI  - Nucleon axial form factor from a Bayesian neural-network analysis of neutrino-scattering data
T2  - Phys. Rev. C
JO  - Physical Review C
SP  - 025204
EP  - 14pp
VL  - 99
IS  - 2
PB  - Amer Physical Soc
AB  - 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.
SN  - 2469-9985
UR  - https://arxiv.org/abs/1805.00905
UR  - https://doi.org/10.1103/PhysRevC.99.025204
DO  - 10.1103/PhysRevC.99.025204
LA  - English
N1  - WOS:000459206200011
ID  - Alvarez-Ruso_etal2019
ER  -