Neutrino Interactions with Nucleons and Nuclei: Importance for Long-Baseline Experiments

Abstract

This article reviews our present knowledge of neutrino interactions with nucleons and discusses the interactions with nuclei, the target material of all presently running and planned long-baseline experiments. I emphasize descriptions of semi-inclusive reactions and full descriptions of the final state; the latter are needed to reconstruct the incoming neutrino energy from final-state observations. I then discuss Monte Carlo generator and more advanced transport theoretical approaches in connection with experimental results on various reaction mechanisms. Finally, I describe the effects of uncertainties in the reconstruction of the incoming neutrino energy on oscillation parameters.

Neutrino-induced one-pion production revisited: The νμn→μ−nπ+Hernández1, Nieves2 2017Phys. Rev. D281441View full textAdd to dashboardCite

“…These are derivative couplings that preserve the gauge invariance of the free massless spin 3/2 Lagrangian. 5 Note that in Ref. [36], the theoretical predictions are below data when the cut W πN < 1.4 GeV is implemented.…”

Constraining the GENIE model of neutrino-induced single pion production using reanalyzed bubble chamber data

“…In this work, we extend that method to include the subdominant ν μ n → μ − pπ 0 and ν μ n → μ − nπ + channels, and we use the resulting data, along with the Q 2 -spectra (where Q 2 is the four-momentum transfer) from the same experiments, to constrain the parameters of the GENIE single pion production model [18]. While more sophisticated single pion production models exist [19][20][21], the GENIE generator is widely used by current and planned neutrino oscillation experiments, so tuning the generator parameters represents a pragmatic approach to improving its description of available data. We find that the reanalyzed data, where the normalization discrepancy has been resolved, is able to significantly reduce the uncertainties on the pion production parameters.…”

Deep Learning for Vertex Reconstruction of Neutrino-nucleus Interaction Events with Combined Energy and Time Data

“…For segment classification with only timing or energy data, we use the same network as shown in Fig.3(a), where the input is a tensor of X, U, V views of timing or energy data, but not both simultaneously as in the previous section. Thus, the input tensor size is (3,127,94). Table 2 shows the segment classification accuracy of our model when both timing and energy data were used (combined) and when only timing or energy data was used.…”