Würzburg 2018 – scientific programme

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T: Fachverband Teilchenphysik

T 18: DAQ / Trigger I

T 18.5: Talk

Monday, March 19, 2018, 17:00–17:15, Z6 - SR 2.010

Background Suppression with the Belle II Neural Network Trigger — •Sebastian Skambraks, Christian Kiesling, and Sara Pohl — Max-Planck-Institut für Physik, München

Neural networks are going to be used in the pipelined first level trigger of the upgraded flavor physics experiment Belle II at the high luminosity B factory SuperKEKB in Tsukuba, Japan. An instantaneous luminosity of L = 8 × 10³⁵cm⁻²s⁻¹ is anticipated, 40 times larger than the world record reached with the predecessor KEKB. Background tracks, with vertices displaced along the beamline (z-axis), are expected to be severely increased due to the high luminosity. Using hit and drift time information from the central drift chamber, the online neural network trigger provides 3D track reconstruction of single tracks. In particular, the robust estimation of the z-vertices significantly improves the suppression of background tracks with vertices outside of the interaction region. This allows to relax the track trigger conditions and thus enhances the efficiency for events with a low track multiplicity. Based on Monte Carlo events and background, the high event rate faced by the first level trigger is analyzed and the benefits of the neural network trigger are evaluated.