Energy scale non-linearity and event reconstruction for the neutrino mass ordering measurement of the JUNO experiment

Abstract

The JUNO experiment is a next-generation neutrino experiment under construction in vicinity of the Pearl River Delta in Southern China. It is expected to start data-taking in 2022 and aims to address the determination of the Neutrino Mass Ordering with 3-4 $\sigma$ sensitivity in about 6 years as its main goal. For that, it will measure the oscillated energy spectrum of electron anti-neutrinos from two nuclear power plants at a baseline of about 53 km with a required energy resolution of 3 % at 1 MeV and a sub-percent uncertainty on the energy scale. In order to reach these requirements, the JUNO detector consists of a large 20 kton liquid scintillator detector, which is instrumented with a dense PMT array consisting of about 18,000 large 20''-PMT's and 25,000 small 3''-PMT's. Besides this main goal it aims to address a large variety of important topics in neutrino and astroparticle physics.The first part of this thesis gives an overview over the current status of neutrino physics and shows why the determination of the Neutrino Mass Ordering is a key to explore a large area of physics topics. Moreover, it gives an overview of the JUNO experiment: the detector design and its calibration, the simulation framework, and the various physics goals of the JUNO experiment. Besides the detector design, a meticulous data analysis is needed to ensure, that the JUNO experiment can meet the requirements on the precision and accuracy on the reconstructed energy. Such analysis methods are presented in the second part of this thesis. Here, a model is presented, which can be used to describe the non-linear light response of positrons in the liquid scintillator. Based on the non-linearity model of electrons, an algorithm is introduced to calculate the more complex non-linearity model of gammas and combine both eventually to the non-linearity model of positrons. As the amount of detected light for a constant energy varies with the position of the energy deposition in the detector, the energy resolution of JUNO is impacted by the uncertainty of the reconstructed light emission vertex. The vertex reconstruction finds the light emission vertex by minimizing a likelihood function, which contains the information on the times and charges of the PMT hits. It is shown, that the uncertainty on the reconstructed vertex is especially small at the outer parts of the volume, where the effect on the energy resolution is the largest. Additionally to the improvement of the energy resolution, it is shown how the vertex reconstruction can be used to reconstruct the direction of an electron anti-neutrino flux from a point-source. Another important effect, which leads to biases on the reconstructed energy on JUNO is the pile-up of signal events with $^{14}$C decays.The organic scintillator contains large amounts of natural, radioactive $^{14}$C. These $^{14}$C decays are able to timely coincide with measured signal events to cause a smearing of the measured energy spectrum. To reduce the impact of these $^{14}$C decays, two analysis methods are presented. A clusterization algorithm identifies different energy depositions in the PMT hit time distribution. This algorithm is optimized on the sensitivity of JUNO to determine the Neutrino Mass Ordering. For event coincidences, which can not be separated in time, the vertex reconstruction is used to perform a likelihood test to identify these