Testing charged current quasi-elastic and multinucleon interaction models in the NEUT neutrino interaction generator with published datasets from the MiniBooNE and MINERνA experiments

There has been a great deal of theoretical work on sophisticated charged current quasi-elastic (CCQE) neutrino interaction models in recent years, prompted by a number of experimental results that measured unexpectedly large CCQE cross sections on nuclear targets. As the dominant interaction mode at T2K energies, and the signal process in oscillation analyses, it is important for the T2K experiment to include realistic CCQE cross section uncertainties in T2K analyses. To this end, T2K’s Neutrino Interaction Working Group has implemented a number of recent models in NEUT, T2K’s primary neutrino interaction event generator. In this paper, we give an overview of the models implemented and present fits to published ${\nu }_{\mu }$ and ${\overline{\nu }}_{\mu }$ CCQE cross section measurements from the MiniBooNE and $\mathrm{MINER}\nu \mathrm{A}$ experiments. The results of the fits are used to select a default cross section model for future T2K analyses and to constrain the cross section uncertainties of the model. We find strong tension between datasets for all models investigated. Among the evaluated models, the combination of a modified relativistic Fermi gas with multinucleon CCQE-like interactions gives the most consistent description of the available data.

Figure 1

The probability distribution for initial state protons within an oxygen nucleus for Benhar’s SF model [23] as a function of the removal energy ($E_{\mathrm{R}}$) and the magnitude of the nucleon momentum ($|\mathbf{p}|$). The SF is normalized such that the integral of this distribution is 1.

Figure 2

SF parameters in NEUT that may be modified on the SF initial state momentum distribution. This figure has been adapted from Ref. [52].

Figure 3

The ratio of the CCQE cross section including the nonrelativistic RPA model to the CCQE cross section without RPA, shown for both muon neutrino and muon antineutrino interactions on carbon. An enhancement of the ratio can be seen at high $Q^2$, and a suppression can be seen at low $Q^2$ (and close to the kinematic boundary for antineutrinos). These $E_{\nu }$—and $Q^2$-dependent tables are used in NEUT to apply the RPA model. For these plots, an axial mass value of $M_{\mathrm{A}}=1.01\mathrm{GeV}/c^2$ was used.

Figure 4

The ratio of the nonrelativistic RPA correction to the relativistic RPA correction, shown for both muon neutrino and muon antineutrino interactions on carbon. These $E_{\nu }$—and $Q^2$-dependent tables are used to reweight NEUT events from one RPA model to the other. By default, NEUT events are generated with the nonrelativistic RPA model.

Figure 5

Cross-correlation matrix including both shape and normalization uncertainties for the $\mathrm{MINER}\nu \mathrm{A}$ neutrino and antineutrino samples. The eight neutrino and eight antineutrino bins shown here correspond to the eight $Q_{\mathrm{QE}}^2$ bins from the $\mathrm{MINER}\nu \mathrm{A}$ datasets.

Figure 6

Nominal model predictions for the $\mathrm{MINER}\nu \mathrm{A}$ datasets with $M_{\mathrm{A}}=1.01\mathrm{GeV}/c^2$ and all other model parameters at their default values. The relativistic RPA model is shown.

Figure 7

Nominal model predictions for the MiniBooNE single-differential datasets with $M_{\mathrm{A}}=1.01\mathrm{GeV}/c^2$ and all other model parameters at their default values. The relativistic RPA calculation is shown. Normalization parameters are applied as given in Table 3.

Figure 8

Nominal model predictions for the MiniBooNE double-differential datasets with $M_{\mathrm{A}}=1.01\mathrm{GeV}/c^2$ and all other model parameters at their default values. The relativistic RPA calculation is shown. Normalization parameters are applied as given in Table 3.

Figure 9

Nominal model predictions for the MiniBooNE double-differential datasets with $M_{\mathrm{A}}=1.01\mathrm{GeV}/c^2$ and all other model parameters at their default values. Note that for each model, the relevant MiniBooNE normalization parameter has been allowed to vary to minimize the ${\chi }^2$ value; however, the scaling factors (given in Table 3) have not been applied in this figure. The relativistic RPA calculation is shown.

Figure 10

Comparison of the best fit from the combined fits detailed in Table 4 with the $\mathrm{MINER}\nu \mathrm{A}$ datasets used in the fit. The ${\chi }^2$ values in the legend are the contribution from each dataset at the best-fit point (and the total ${\chi }_{\min }^2$ for the combined fit).

Figure 11

Comparison of the best fit from the combined fits detailed in Table 4 with the MiniBooNE double-differential datasets used in the fit. The ${\chi }^2$ values in the legend are the contribution from each dataset at the best-fit point (and the total ${\chi }_{\min }^2$ for the combined fit). The thick lines have the MiniBooNE normalization factors applied (given in Table 4), while the thin lines do not, to indicate the large pulls on these parameters.

Figure 12

Correlation matrix for the best-fit $\mathrm{RFG}+\text{rel}\mathrm{RPA}+2\mathrm{p}2\mathrm{h}$ model parameters.