Simulation of the Front End electronics chain of the LEGEND-200 experiment

The LEGEND experiment searches for neutrinoless double-beta decay (0νββ), a process that, if observed, would confirm that neutrinos are Majorana particles. The experiment employs germanium detectors enriched in 76Ge, an isotope that undergoes double-beta decay. The goal is to identify a potential 0νββ signal within an energy spectrum dominated by background events, despite the implementation of both passive and active background reduction strategies. To achieve this, various event selection techniques are applied, including Pulse Shape Discrimination (PSD), which analyzes waveform shapes to reject background events that would otherwise mimic the signal in energy and topology. This thesis focuses on developing a Python-based simulation of the LEGEND-200 chargesensitive amplifier (CSA) at its cryogenic operating temperature. The simulation addresses a missing component in the Pulse Shape Simulation framework used by the LEGEND collaboration, enabling the generation of realistic simulated waveforms that closely resemble experimental data. By incorporating this simulation into the analysis pipeline, traditional and machine-learning-based PSD techniques can be optimized for improved event selection. Furthermore, the use of simulated data will facilitate the determination of selection efficiencies and associated uncertainties, a key aspects currently missing in the analysis of LEGEND data.