TY  - GEN
AV  - public
TI  - Application of Artificial Neural Networks in GERDA for the search of neutrinoless double ?-decay of 76Ge
UR  - https://archiv.ub.uni-heidelberg.de/volltextserver/35548/
A1  - Bothe, Vikas Shridhar
CY  - Heidelberg
Y1  - 2024///
N2  - 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 (0???) in 76 Ge. 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 0??? 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 Q?? by ~65%, achieving a background index of 8.3 × 10^-3 cts/(keV. kg. yr ) and 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 0??? decay of 76Ge is set at T1/2 > 1.8 × 10^26 yr at 90% C.L and the sensitivity coincides with the limit.
ID  - heidok35548
ER  -