Nucleon axial form factor from a Bayesian neural-network analysis of neutrino-scattering data

Abstract

The Bayesian approach for feed-forward 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 modelindependent determination of the axial form factor from data. When the low 0.05 < Q 2 < 0.10 GeV 2 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.

Neural network study of the nucleon axial form-factor

“…The height of the F A local maximum is reduced once the deuteron correction is included; this maximum disappears when the first bin has been removed from the ANL data. The value of r A for the best fit corresponds to the BIN2 data and has a value compatible with other determinations such as the z-expansion ones from bubble chamber data [9] and from muon capture by protons [10] but with a significantly smaller error [1]. We realize that deuteron effects are sizable by looking at the changes in the behavior of F A at low Q 2 when we add the deuteron corrections and when we take out the first bins of the data.…”

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We have performed the first Bayesian neural-network analysis of neutrino-deuteron scattering data [1]. The nucleon axial form factor has been extracted from quasielastic scattering data collected by the Argonne National Laboratory (ANL) bubble chamber experiment using a modelindependent parametrization.

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Nucleon axial form factor from generalized parton distributions

“…(12) has only a single free parameter, M A , which should be fixed by fitting the experimental data. It should be also noted that, in the Breit frame and for small momenta, such Q 2 dependence of AFF leads to an exponentially decrease for the axial charge distribution [55]. However, from a theoretical point of view, it has been indicated that this ansatz is not a good choice, e.g.…”

“…In particular, the authors of Ref. [55] have used this tool to analyze the neutrino-deuteron scattering data measured by the Argonne National Laboratory (ANL) bubble chamber experiment.…”

New evaluation of the axial nucleon form factor from electron- and neutrino-scattering data and impact on neutrino-nucleus cross sections

“…In a non relativistic approach, the dipole distribution is the Fourier transform of an exponential charge distribution. Other models and parametrizations are available (axial-Vector dominance [22], neural-network Bayesian analyses [23]) as well as recent results from lattice QCD [24]. In this letter the axial form factor is calculated in frame of Vector Meson Dominance (VDM).…”