Development of topological data analysis methods for AGATA

Student: Fraser Holloway
Supervisor: Laura Harkness-Brennan


‌AGATA is a next generation detector system for studies of nuclear structure. To benefit from its unrivalled peak-to-total energy resolution and efficiency, advanced data analysis (PSA and tracking) algorithms are used to identify the positions of gamma-ray interactions so that each gamma-ray can be tracked through the 180-detector array. AGATA will have 6,840 channels, continuously digitised by sampling 14-bit ADCs at 100MHz. Following pre-processing, 600 ns data packages are sent to server farms that run pulse shape analysis algorithms with data rates up to 370 MB/s. These are used to determine the gamma-ray interaction positions, which are fed into tracking algorithms and Grid computing. They search through an experimentally validated simulated preamplifier signal database to find the best match with observed signals. Machine learning techniques will be used to learn how to enhance the performance of pulse shape data analysis algorithms with Daresbury NP. An extended secondment to Hartree will allow broadening the students’ awareness of wider applications in data intensive science. An approach accumulating recent interest internationally and in LBDN in such contexts is the use of MC for search alongside Deep Learning.