Münster 2017 – scientific programme

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T: Fachverband Teilchenphysik

T 71: Trigger und DAQ 1

T 71.4: Talk

Tuesday, March 28, 2017, 17:30–17:45, VSH 05

Background suppression with neural networks at the Belle II trigger — •Sebastian Skambraks, Sara Neuhaus, and Christian Kiesling — Max-Planck-Institut für Physik, München

We present the neural network based first level track trigger for the upcoming Belle II detector at the high luminosity SuperKEKB flavor factory. Using hit and drift time information from the Central Drift Chamber (CDC), neural networks estimate the z-coordinates of single track vertex positions. Beam induced background, with vertices outside of the interaction region, can clearly be rejected. This allows to relax the track trigger conditions and thus enhances the efficiency for events with a low track multiplicity.

The expected performance of the neural networks is evaluated on simulated events and background. This involves a full machine simulation of the SuperKEKB accelerator using the physics models of the expected background types. After an introduction to the neural trigger system, the expected background types are introduced and their suppression will be discussed.