A neural network z-vertex trigger for Belle II

TL;DR

This paper introduces a neural network-based track trigger for the Belle II experiment that estimates the event's z-vertex within trigger latency, enabling effective background suppression without explicit track reconstruction.

Contribution

It presents a novel neural network approach integrated into the Belle II trigger system to estimate the z-vertex rapidly using CDC data, improving background rejection capabilities.

Findings

Neural network accurately estimates z-vertex within trigger latency.

The trigger suppresses background events outside the interaction region.

Integration with existing CDC trigger enhances real-time vertex estimation.

Abstract

We present the concept of a track trigger for the Belle II experiment, based on a neural network approach, that is able to reconstruct the z (longitudinal) position of the event vertex within the latency of the first level trigger. The trigger will thus be able to suppress a large fraction of the dominating background from events outside of the interaction region. The trigger uses the 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 ($r - \varphi$) 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 a continuous output corresponding to the scaled z-vertex. The input values for the neural network are calculated from the wire hits of the CDC.