Aug 21 – 25, 2017
University of Washington, Seattle
US/Pacific timezone

Background Suppression with the Belle II Neural Network Trigger

Aug 21, 2017, 2:00 PM
107 (Alder Hall)


Alder Hall

Oral Track 2: Data Analysis - Algorithms and Tools Track 2: Data Analysis - Algorithms and Tools


Sebastian Skambraks (Technische Universität 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. A luminosity of $\mathcal{L} = 8 \times 10^{35}\,cm^{−2} s^{−1}$ 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 input from the central drift chamber, the main tracking device of Belle II, the online neural network trigger provides 3D track reconstruction within the fixed latency of the first level trigger. In particular, the robust estimation of the $z$-vertices allows a significantly improved suppression of the machine background. Based on a Monte Carlo background simulation, the high event rate faced by the first level trigger is analyzed and the benefits of the neural network trigger are evaluated.

Primary authors

Sebastian Skambraks (Technische Universität München) Sara Neuhaus Christian Kiesling (Werner-Heisenberg-Institut)

Presentation materials

Peer reviewing