Gießen 2024 – scientific programme
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HK: Fachverband Physik der Hadronen und Kerne
HK 1: Computing I
HK 1.7: Talk
Monday, March 11, 2024, 18:15–18:30, HBR 14: HS 1
Machine Learning Algorithms for Pattern Recognition with the PANDA Barrel DIRC — •Yannic Wolf1,2, Roman Dzhygadlo1, Klaus Peters1,2, Georg Schepers1, Carsten Schwarz1, and Jochen Schwiening1 for the PANDA collaboration — 1GSI Helmholtzzentrum für Schwerionenforschung GmbH, Darmstadt — 2Goethe-Universität Frankfurt
Precise and fast hadronic particle identification (PID) is crucial to reach the physics goal of the PANDA detector at FAIR. The Barrel DIRC (Detection of Internally Reflected Cherenkov light) is a key detector for the identification of charged hadrons in PANDA. Several reconstruction algorithms have been developed to extract the PID information from the measured location and arrival time of the Cherenkov photons. In comparison to other Ring Imaging Cherenkov detectors, the hit patterns observed with DIRC counters do not appear as rings on the photosensor plane but as complex, disjoint 3D-patterns.
Using the recent advances in machine learning (ML) algorithms, especially in the area of image recognition, we plan to develop new ML PID algorithms for the PANDA Barrel DIRC and compare the results to conventional reconstruction methods. First network implementations show a performance comparable to conventional methods on a limited phase space. As a next step, we are investigating ways to extend the phase space, while also experimenting with different data input structures to optimize the training process and increase PID performance.
Keywords: AI; Machine Learning; PANDA; DIRC