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