000004347 001__ 4347
000004347 005__ 20240526150601.0
000004347 037__ $$aBELLE2-MTHESIS-2024-018
000004347 041__ $$aeng
000004347 100__ $$aSimon Hiesl
000004347 245__ $$aUpgrade of Belle II's Neural Network Trigger by Track Finding in Three-Dimensional Hough Space
000004347 260__ $$aMunich$$bMax-Planck-Institute for Physics$$c2024
000004347 300__ $$a151
000004347 500__ $$aPresented on 12 04 2024
000004347 502__ $$aMSc$$bMunich, Ludwig-Maximilians-University$$c2024
000004347 520__ $$aThe target luminosity of the Belle II experiment, located at the SuperKEKB electron-positron collider in Tsukuba, Japan, is 6x10^{35} cm^{-2}s^{-1}. For this world-record luminosity, high levels of beam background are expected. Thus, an efficient and robust trigger system at the hardware level, capable of selecting annihilation events from the interaction point ("z=0"), is indispensable. For this purpose, a novel track trigger using the wire information of the central drift chamber predicts the z-vertex utilizing a neural network. While this trigger is very successful in suppressing background, recent high luminosity physics runs produced many fake tracks, increasing the trigger rate close to the maximum limit. To keep the L1 trigger rate below the design value of $30 \kHz$, an upgrade of the Neuro Trigger is proposed in this work. In this thesis, the preselection algorithm for the neural network, called the 3DFinder, which creates three-dimensional track candidates using a three-dimensional Hough transformation, is extensively analyzed and upgraded. For the analysis, both simulated Monte Carlo data and real data from the latest Belle II runs are utilized. The upgrade includes a new clustering algorithm and new parameters to reduce the number of fake tracks. The proposed new algorithms for the 3DFinder allow for an implementation on the trigger hardware while achieving high efficiency for single IP tracks and a low fake rate.
000004347 700__ $$aProf. Dr. Christian Kiesling$$edir.
000004347 8560_ $$fhiesl@mpp.mpg.de
000004347 8564_ $$uhttps://docs.belle2.org/record/4347/files/BELLE2-MTHESIS-2024-018.pdf
000004347 980__ $$aTHESIS