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Author  |
Alvarez-Ruso, L.; Graczyk, K.M.; Saul-Sala, E. |
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Title |
Nucleon axial form factor from a Bayesian neural-network analysis of neutrino-scattering data |
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Journal Article |
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Year |
2019 |
Publication |
Physical Review C |
Abbreviated Journal |
Phys. Rev. C |
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Volume |
99 |
Issue |
2 |
Pages |
025204 - 14pp |
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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. |
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Address |
[Alvarez-Ruso, Luis; Saul-Sala, Eduardo] Ctr Mixto UVEG CSIC, Dept Fis Teor, Valencia, Spain |
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Publisher |
Amer Physical Soc |
Place of Publication |
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Language |
English |
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Original Title |
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Series Volume |
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Edition |
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ISSN |
2469-9985 |
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Notes |
WOS:000459206200011 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
3915 |
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