Speaker
Description
As more measurements on various event topologies of neutrino-nucleus interactions become available, Monte Carlo (MC) prediction from neutrino event generators, such as GENIE, shows considerable deviation from some data sets while matching others relatively well. In this work, we present the first global analysis, enabled by the GENIE global analysis framework, of four Transverse Kinematic Imbalance (TKI) measurements on carbon targets with and without pion production, namely $\nu_\mu CC0\pi Np$ and $\nu_\mu CC1\pi^+ Np$ from T2K and $\nu_{\mu}CC0\pi Np$ and $\nu_\mu CC M\pi^0 Np$ from MINERvA, where $M,N\geq1$. In both T2K and MINERvA data releases, the authors benchmarked their data against GENIE predictions, except for MINERvA’s $\pi^0$, in which a GENIE comparison is absent. In the reported benchmarking, GENIE predictions agree relatively well with T2K’s $\nu_\mu CC0\pi Np$ and $\nu_\mu CC1\pi^+ Np$ as well as MINERvA’s $\nu_\mu CC0\pi Np$ measurements. We use the GENIE global analysis framework with the latest GENIE tune, G24_20i_00_000. The prediction describes the first three measurements considerably well, but it overpredicts MINERvA’s $\pi^0$. As the TKI variables are excellent probes for nuclear initial states and final state interactions (FSI), we performed a tuning on the reliable Local Fermi Gas (LFG) nuclear model and the hA INTRANUKE FSI model using GENIE Comparisons interfaced with PROFESSOR. The tuning exercise serves as a proof-of-concept practice of using TKI data with and without pion production to constrain model parameters, and the result is stored in a new GENIE tune, G24_20i_06_22c. It gives significantly better prediction for the single $\pi^0$ production sample while keeping the same level of data-MC agreement for $0\pi$ and $1\pi^+$ datasets. This is accomplished mainly by suppressing $\pi^0$ mean free path and the pion charge exchange in FSI. The success of this tuning bolsters the viability of our method and highlights a workable path towards continual model tuning.
| Poster prize | Yes |
|---|---|
| Given name | Julia |
| Surname | Tena Vidal |
| First affiliation | University of Tel Aviv |
| Institutional email | jtenavidal@tauex.tau.ac.il |
| Gender | Female |
| Collaboration (if any) | GENIE |
