Introduces Laplace-approximated Bayesian PINNs for automatic loss-weight optimization when solving PDEs such as heat, wave, and Burgers equations.
Nucleon axial form factor from a Bayesian neural-network analysis of neutrino-scattering data
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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 (ANL) bubble chamber experiment. This framework allows to perform a model-independent determination of the axial form factor from data.. When the low $0.05 < Q^2 < 0.10$ GeV$^2$ data is 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 the dipole ansatz 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.
verdicts
UNVERDICTED 2representative citing papers
NNLO ChPT with explicit Delta fits lattice data to extract g_A = 1.257 ± 0.011 and axial radius squared 0.312 ± 0.037 fm² at the physical point.
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Bayesian Reasoning for Physics Informed Neural Networks
Introduces Laplace-approximated Bayesian PINNs for automatic loss-weight optimization when solving PDEs such as heat, wave, and Burgers equations.