PoS - Proceedings of Science
Volume 372 - Artificial Intelligence for Science, Industry and Society (AISIS2019) - Day 1
Online Estimation of Particle Track Parameters based on Neural Networks for the Belle II Trigger System
S. Baehr,* K.L. Unger, J. Becker, F. Meggendorfer, S. Skambraks, C. Kiesling
*corresponding author
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Pre-published on: July 10, 2020
Published on:
Abstract
The Belle II particle accelerator experiment is experiencing substantial background from outside of the interaction point. To avoid taking data representing this background, track parameters are estimated within the pipelined and dead time-free level 1 trigger system of the experiment and used to suppress such events. The estimation of a particle track's origin with respect to the z-Axis, which is along the beamline, is performed by the neural z-Vertex trigger. This system is estimating the origin or z-Vertex using a trained multilayer perceptron, leveraging the advantages of training to current circumstances of operation. In order fulfil the requirements set by the overall trigger system it has to provide the estimation within an overall latency of 5 us while matching a refresh rate of up to 31.75 for new track estimations. The focus of this contribution is this system' current status. For this both implementation and integration into the level 1 trigger will be presented, supported by first data taken during operation as well as figures of merit such as latency and resource consumption. In addition its upgrade plan for the near future will be presented. The center of these is a Hough based track finding approach that uses Bayes theorem for training the weighting of track candidates. Characteristics of this system's current prototypical implementation on FPGAs as well as present plants towards integration for future operation will be presented.
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