Open Access

Tobias Habermann

• High resolution gamma spectroscopy with sophisticated detector arrays significantly contributes to nuclear structure physics. The Advanced Gamma Tracking Array (AGATA) combines gamma tracking and pulse shape analysis to achieve an efficiency and quality of the spectra that could not be reached with spectrometers of the previous generation. Tracking of the photons interacting in the detector requires a precise knowledge of the individual interaction positions. The task of the pulse shape analysis is to provide a position resolution of better than $5mm$ FWHM, a value that could not be achieved by segmentation of the detector alone. As the signals induced on the electrodes of the detectors depends on the position of interaction, the charge pulses can be used to infer the interaction position. To be able to handle high rates, algorithms that are used have to be optimized to be able to process the data in real-time. Pulse shape analysis is the most involved part of the real-time processing and requires further improvement. This work is dealing with optimizations and improvements of pulse shape analysis algorithms. The Grid Search algorithm localizes the interaction position by comparing the measured pulse shape with precomputed shapes in a database to find the best fit. Two linear filters based on orthogonal transformations have been compared and it could be concluded that the one based on a singular value decomposition of the pulse shapes works best. It speeds up the pulse shape analysis by a factor of roughly $2-3$ (depending on how it is combined with the other modifications). Further, a new method to exclude most signals from the database as best fit has been developed based on the principle of lateration. Most interaction positions can be excluded by means of a fast check and for single interactions on average only $34.8\%$ of all signals from the database have to be compared to the measured one. The overhead introduced by the method is negligible and the reduced number of comparisons almost direclty translates into increased efficiency of the algorithm. A similar method could also be applied for double interactions. Two or more interactions taking place in the same segment require special treatment as the measured signals cannot be directly compared to signals from the database. A new method to calculate the figure of merit that quantifies the fit in case of a double interaction has been introduced. Compared to the unmodified algorithm the new method finds the best fit for double interactions roughly two orders of magnitude faster. Actually, the time required to localize double interactions is almost the same as for single interactions. Apart from optimizing the algorithm, also the achievable position resolution was investigated. It strongly varies inside the volume of the detector and it crucially depends on the shape of all signals in the database and the amplitude of the noise present in the measured signals. As a first step towards a precise analytic expression for the position resolution, an estimate for the probability to find the correct position has been derived.