Towards a realistic track reconstruction algorithm based on graph neural networks for the HL-LHC

TL;DR

This paper presents new graph neural network algorithms for realistic charged particle track reconstruction in HL-LHC detectors, aiming to improve efficiency and deployability within experimental frameworks.

Contribution

It introduces advanced GNN-based algorithms capable of handling complex realistic detectors, integrated into the ACTS tracking software for practical deployment.

Findings

Algorithms handle complex detector geometries.

Implemented within ACTS framework.

Aims for efficient, realistic track reconstruction.

Abstract

The physics reach of the HL-LHC will be limited by how efficiently the experiments can use the available computing resources, i.e. affordable software and computing are essential. The development of novel methods for charged particle reconstruction at the HL-LHC incorporating machine learning techniques or based entirely on machine learning is a vibrant area of research. In the past two years, algorithms for track pattern recognition based on graph neural networks (GNNs) have emerged as a particularly promising approach. Previous work mainly aimed at establishing proof of principle. In the present document we describe new algorithms that can handle complex realistic detectors. The new algorithms are implemented in ACTS, a common framework for tracking software. This work aims at implementing a realistic GNN-based algorithm that can be deployed in an HL-LHC experiment.