Vertex Reconstructing Neural Network at the ZEUS Central Tracking Detector

TL;DR

This paper introduces a neural network-based method for accurately reconstructing the interaction point in high-energy physics detectors, outperforming traditional algorithms on simulated ZEUS detector data.

Contribution

It presents a novel neural network approach with fixed architecture for vertex reconstruction, demonstrating improved accuracy over conventional methods.

Findings

Neural network method achieves higher accuracy.

System performs well on simulated ZEUS data.

Outperforms traditional algorithms in vertex reconstruction.

Abstract

An unconventional solution for finding the location of event creation is presented. It is based on two feed-forward neural networks with fixed architecture, whose parameters are chosen so as to reach a high accuracy. The interaction point location is a parameter that can be used to select events of interest from the very high rate of events created at the current experiments in High Energy Physics. The system suggested here is tested on simulated data sets of the ZEUS Central Tracking Detector, and is shown to perform better than conventional algorithms.