ausblenden:
Schlagwörter:
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Zusammenfassung:
The GERmanium Detector Array (GERDA) experiment, located underground at the Laboratori Nazionali del Gran Sasso (LNGS) in Italy, is dedicated to the search for neutrinoless double beta decay (0νββ) in 76Ge. Such a discovery would provide evidence that neutrinos are Majorana particles and challenge the Standard Model of particle physics by violating lepton number conservation. This thesis presents the development and application of robust artificial neural network (ANN)-based classification models for pulse shape discrimination (PSD) within the GERDA experiment, specifically tailored for the high-purity germanium (HPGe) detectors of semi-coaxial geometry. The goal is to improve the experimental sensitivity to 0νββ events by suppressing the background in GERDA. The semi-coaxial detectors represent ~49% of total 127.2 kg.yr exposure in GERDA. For each of the semi-coaxial detectors, 1-d CNN-based models were trained for classification tasks to discriminate the surface and gamma-induced backgrounds, which suppress the background index at Qββ by ~65%, achieving a background index of 8.3 × 10-3 cts/(keV. kg. yr 0. 59 × 10−3 cts/(keV. kg. yr) in Phase I and PhaseII, respectively. No signal is observed, and a limit on the half-life of 0νββ decay of 76Ge is set at T1/20υ > 1. 8 × 1026 yr at 90% C.L and the sensitivity coincides with the limit.
