Momentum Reconstruction and Triggering in the TALAS Detector

TL;DR

This paper presents a neural network approach for reconstructing muon momentum and charge in the ATLAS detector, demonstrating efficiency and potential for real-time triggering in high energy physics experiments.

Contribution

It introduces a neural network method for momentum and charge reconstruction using limited detector data, suitable for fast triggering systems in particle physics.

Findings

Neural network effectively reconstructs muon momentum and charge.

Method is efficient for rapid classification in triggering systems.

Parallel processing makes it suitable for hardware implementation.

Abstract

A neural network solution for a complicated experimental High Energy Physics problem is described. The method is used to reconstruct the momentum and charge of muons produced in collisions of particle in the ATLAS detector. The information used for the reconstruction is limited to the output of the outer layer of the detector, after the muons went through strong and inhomogeneous magnetic field that have bent their trajectory. It is demonstrated that neural network solution is efficient in performing this task. It is shown that this mechanism can be efficient in rapid classification as required in triggering systems of the future particle accelerators. The parallel processing nature of the network makes it relevant for hardware realization in the ATLAS triggering system.