EPJ Web Conf.
Volume 150, 2017Connecting The Dots/Intelligent Trackers 2017 (CTD/WIT 2017)
|Number of page(s)||9|
|Published online||08 August 2017|
The track finding algorithm of the Belle II vertex detectors
1 Charles University, Prague (Czech Republic)
2 IEKP, Karlsruhe Institute of Technology, Karlsruhe (Germany)
3 I.N.F.N. sezione di Pisa, Pisa (Italy)
4 Università di Pisa, Pisa (Italy)
5 Institut für Kernphysik Johannes Gutenberg-Universität, Mainz (Germany)
6 Deutsches Elektronen-Synchrotron, Hamburg, (Germany)
7 Institute of High Energy Physics, Vienna (Austria)
8 Scuola Normale Superiore di Pisa, Pisa (Italy)
9 Excellencecluster Universe, Ludwigs-Maximillians-University (Germany)
10 Physikalisches Institut Rheinische Friedrich-Wilhelms-Universität, Bonn (Germany)
11 Excellencecluster Universe, Ludwigs-Maximillians-University, Munich (Germany)
a e-mail: firstname.lastname@example.org
Published online: 8 August 2017
The Belle II experiment is a high energy multi purpose particle detector operated at the asymmetric e+e− - collider SuperKEKB in Tsukuba (Japan). In this work we describe the algorithm performing the pattern recognition for inner tracking detector which consists of two layers of pixel detectors and four layers of double sided silicon strip detectors arranged around the interaction region. The track finding algorithm will be used both during the High Level Trigger on-line track reconstruction and during the off-line full reconstruction. It must provide good efficiency down to momenta as low as 50 MeV/c where material effects are sizeable even in an extremely thin detector as the VXD. In addition it has to be able to cope with the high occupancy of the Belle II detectors due to the background. The underlying concept of the track finding algorithm, as well as details of the implementation are outlined. The algorithm is proven to run with good performance on simulated ϒ(4S) → BB̄ events with an efficiency for reconstructing tracks of above 90% over a wide range of momentum.
© The Authors, published by EDP Sciences, 2017
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