Model Dependence of the Properties of S11 Baryon Resonances

The properties of baryon resonances are extracted from a complicated process of fitting sophisticated, empirical models to data. The reliability of this process comes from the quality of data and the robustness of the models employed. With the large of amount of data coming from recent experiments, this is an excellent time for a study of the model dependence of this extraction process. A test case is chosen where many theoretical details of the model are required, the S11 partial wave. The properties of the two lowest N* resonances in this partial wave are determined using various models of the resonant and non-resonant amplitudes.Comment: 24 pages, 10 figures; revised fits with error estimates, expanded comparison between CMB and K-matrix model

Impact of recent data on N* structure

Many modern experiments are posed with the issue of physics interpretation of their data when the theory is complicated. Certainly, experiments studying N* resonances are in this category. This short paper presents examples of interpretation made by inspection of the data, Breit-Wigner analyses, and coupled channels analysis. There are significant advantages to all three, but only a coupled channels analysis can provide the checks needed for a complete analysis. Examples from the S11 and P13 partial waves are discussed

Neutrino-nucleus CC0π\mathrm{CC}0{\pi} cross-section tuning in GENIE v3

International audienceThis article summarizes the state of the art of νμ and ν¯μ CC0π cross-section measurements on carbon and argon and discusses the relevant nuclear models, parametrizations and uncertainties in GENIE v3. The CC0π event topology is common in experiments at a few-GeV energy range. Although its main contribution comes from quasielastic interactions, this topology is still not well understood. The GENIE global analysis framework is exploited to analyze CC0π datasets from MiniBooNE, T2K and MINERνA. A partial tune for each experiment is performed, providing a common base for the discussion of tensions between datasets. The results offer an improved description of nuclear CC0π datasets as well as data-driven uncertainties for each experiment. This work is a step towards a GENIE global tune that improves our understanding of neutrino interactions on nuclei. It follows from earlier GENIE work on the analysis of neutrino scattering datasets on hydrogen and deuterium

Neutrino-nucleus CC0π\pi cross-section tuning in GENIE v3

This article summarizes the state of the art of $\nu_\mu$ and $\bar{\nu}_\mu$ CC0$\pi$ cross-section measurements on carbon and argon and discusses the relevant nuclear models, parametrizations and uncertainties in GENIE v3. The CC0$\pi$ event topology is common in experiments at a few-GeV energy range. Although its main contribution comes from quasi-elastic interactions, this topology is still not well understood. The GENIE global analysis framework is exploited to analyze CC0$\pi$ datasets from MiniBooNE, T2K and MINERvA. A partial tune for each experiment is performed, providing a common base for the discussion of tensions between datasets. The results offer an improved description of nuclear CC0$\pi$ datasets as well as data-driven uncertainties for each experiment. This work is a step towards a GENIE global tune that improves our understanding of neutrino interactions on nuclei. It follows from earlier GENIE work on the analysis of neutrino scattering datasets on hydrogen and deuterium

AGKY Hadronization Model Tuning in GENIE v3

The GENIE neutrino Monte Carlo describes neutrino-induced hadronization with an effective model, known as AGKY, which is interfaced with PYTHIA at high invariant mass. Only the low-mass AGKY model parameters were extracted from hadronic shower data from the FNAL 15 ft and BEBC experiments. In this paper, the first hadronization tune on averaged charged multiplicity data from deuterium and hydrogen bubble chamber experiments is presented, with a complete estimation of parameter uncertainties. A partial tune on deuterium data only highlights the tensions between hydrogen and deuterium datasets