1-5 September 2014
Faculty of Civil Engineering
Europe/Prague timezone

A Neural Network z-Vertex Trigger for Belle II

1 Sep 2014, 15:15
25m
C219 (Faculty of Civil Engineering)

C219

Faculty of Civil Engineering

Faculty of Civil Engineering, Czech Technical University in Prague Thakurova 7/2077 Prague 166 29 Czech Republic
Oral Data Analysis - Algorithms and Tools Data Analysis - Algorithms and Tools

Speaker

Mrs Sara Neuhaus (TU München)

Description

The Belle II experiment, the successor of the Belle experiment, will go into operation at the upgraded KEKB collider (SuperKEKB) in 2016. SuperKEKB is designed to deliver an instantaneous luminosity $\mathcal{L} = 8 \times 10^{35}\mathrm{cm}^{-2}\mathrm{s}^{-1}$, a factor of 40 larger than the previous KEKB world record. The Belle II experiment will therefore have to cope with a much larger machine background than its predecessor Belle, in particular from events outside of the interaction region. We present the concept of a track trigger, based on a neural network approach, that is able to suppress a large fraction of this background by reconstructing the $z$ (longitudinal) position of the event vertex within the latency of the first level trigger. The trigger uses the topological and drift time information of the hits from the Central Drift Chamber (CDC) of Belle II within narrow cones in polar and azimuthal angle as well as in transverse momentum (sectors), and estimates the $z$-vertex without explicit track reconstruction. The preprocessing for the track trigger is based on the track information provided by the standard CDC trigger. It takes input from the 2D track finder, adds information from the stereo wires of the CDC, and finds the appropriate sectors in the CDC for each track in a given event. Within each sector, the $z$-vertex of the associated track is estimated by a specialized neural network, with the wire hits from the CDC as input and a continuous output corresponding to the scaled $z$-vertex. The neural algorithm will be implemented in programmable hardware. To this end a Virtex 7 FPGA board will be used, which provides at present the most promising solution for a fully parallelized implementation of neural networks or alternative multivariate methods. A high speed interface for external memory will be integrated into the platform, to be able to store the $\mathcal{O}(10^9)$ parameters required. The contribution presents the results of our feasibility studies and discusses the details of the envisaged hardware solution.

Primary authors

Prof. Alois Knoll (TU München) Prof. Christian Kiesling (MPI für Physik, München) Fernando Abudinen (MPI für Physik, München) Prof. Jochen Schieck (Inst. f. Hochenergiephysik, Wien) Dr Martin Heck (KIT, Karlsruhe) Prof. Michael Feindt (KIT, Karlsruhe) Dr Rudolf Frühwirth (Inst. f. Hochenergiephysik, Wien) Mrs Sara Neuhaus (TU München) Mr Sebastian Skambraks (TU München) Prof. Stephan Paul (TU München) Mr Yang Chen (TU München)

Presentation Materials

Paper