Identification of slow pions by Support Vector Machines Identifizierung von langsamen Pionen durch Support Vector Machines

Sumitted to PubDB: 2022-04-04

Category: Bachelor Thesis, Visibility: Public

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Authors	Jens Lange, Timo Schellhaas
Date	Jan. 1, 2022
Belle II Number	BELLE2-UTHESIS-2022-001
Abstract	Slow 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.
Conference	Giessen