000004124 001__ 4124
000004124 005__ 20240226091344.0
000004124 037__ $$aBELLE2-TALK-CONF-2024-017
000004124 041__ $$aeng
000004124 100__ $$aJohannes Bilk
000004124 245__ $$aSlow pion identification at the Belle II PXD with machine learning
000004124 260__ $$aDPG-Frühjahrstagung Karlsruhe $$c2024-02-26
000004124 300__ $$amult. p
000004124 520__ $$a The identification of slow pions in Belle II experiments presents a no- table challenge, arising from their high dE/dx energy loss and their short flight path in the tracking detectors. This study introduces a method employing advanced machine learning algorithms to accurately detect pions with momentum p<100 MeV/c exclusively with the Belle II pixeldetector (PXD). By analyzing detector signals (in particular a 9x9 pixel matrix) with image processing and pattern recognition methods, this approach significantly boosts the efficiency and accu- racy. Offline and online (FPGA) implemenation will be discussed.
000004124 700__ $$aStephanie Käs
000004124 700__ $$aSören Lange
000004124 700__ $$aElisabetta Prencipe
000004124 700__ $$aTimo Schellhaas
000004124 8560_ $$fjohannes.bilk-2@exp2.physik.uni-giessen.de
000004124 8564_ $$uhttps://docs.belle2.org/record/4124/files/BELLE2-TALK-CONF-2024-017.pdf