000002950 001__ 2950
000002950 005__ 20220404080957.0
000002950 037__ $$aBELLE2-UTHESIS-2022-001
000002950 041__ $$ager
000002950 100__ $$aTimo Schellhaas
000002950 245__ $$aIdentification of slow pions by Support Vector Machines$$bIdentifizierung von langsamen Pionen durch Support Vector Machines
000002950 260__ $$aGiessen$$bII. Institute of Physics$$c2022
000002950 300__ $$amult. p
000002950 500__ $$aPresented on 28 03 2022
000002950 502__ $$aBSc$$bGiessen, Justus-Liebig-University$$c2022
000002950 520__ $$aSlow pions are classified based upon a pattern recognition algorithm using 9x9 pixel matrices in the PXD as input pattern. Background are electrons and positrons from QED processes. Support vector machines are, different from most of the other machine learning techniques, increasing the number of input dimensions, and following separating signal and background by hyperplanes. Values of 78% for correct identification were achieved. 
000002950 700__ $$aJens Sören Lange
000002950 8560_ $$fsoerenlange@yahoo.com
000002950 8564_ $$uhttps://docs.belle2.org/record/2950/files/BELLE2-UTHESIS-2022-001.pdf
000002950 980__ $$aTHESIS