Le lundi 2 décembre 2019 à 14h00 dans l’amphi Becquerel polytechnique
The WAGASCI (WAter Grid And SCIntillator) experiment has for goal to improve the knowledge on the neutrino cross-sections through a measurement of those cross-section on water, on plastic as well as their ratio. The measurement will be done with a very large acceptance thanks to an innovative grid structure as well as the use of Side Muon Range Detectors (side-MRDs). The detectors include both water and plastic targets, which allows to measure the ratio of the two cross-sections with the same neutrino flux. The flux being the main source of systematics errors, the precision of the measurement will be greatly improved in the ratio measurement since most of the systematics will cancel out.\\
In this thesis we will first present the theoretical framework of neutrino oscillation and neutrino interactions with matter. Then we will discuss both T2K and WAGASCI experimental setups and highlight the intermediate setup that has been used for our measurement. We will then describe our Monte-Carlo simulation and CC0pi event selection. Especially we will present the particle identification algorithm used in this selection. We will explain the method we chose to extract the cross-section (the Bayesian unfolding) and the determination of a convergence criterion based on both the Monte-Carlo and the data. In between we will carefully describe both statistical and systematic errors and how we evaluated them as well as their impact on the cross-section measurement. Finally we will present our experimental results on the measurement of the cross-sections on water, on hydrocarbon as well as the ratio of these cross-sections.\\
Because the configuration used in this thesis is not the final one, we cannot yet work with high-angle events. Side-MRDs were not yet installed at the time when the data were taken. Furthermore the muon detector used could not stop high-momentum muons because of a limited depth. However, our intermediate setup will be enough to conduct a comprehensive analysis with sufficient statistics.