Transformers for Charged Particle Track Reconstruction in High Energy Physics

TL;DR

This paper introduces a novel Transformer-based deep learning approach for charged particle track reconstruction in high energy physics, achieving state-of-the-art accuracy and efficiency on the challenging HL-LHC data.

Contribution

It presents a new combined Transformer and MaskFormer architecture specifically designed for particle track reconstruction, outperforming traditional algorithms in accuracy and speed.

Findings

Achieves 97% efficiency with 0.6% fake rate on TrackML dataset

Inference time of 100ms per event

Flexible approach adaptable for various applications like triggering systems

Abstract

Reconstructing charged particle tracks is a fundamental task in modern collider experiments. The unprecedented particle multiplicities expected at the High-Luminosity Large Hadron Collider (HL-LHC) pose significant challenges for track reconstruction, where traditional algorithms become computationally infeasible. To address this challenge, we present a novel learned approach to track reconstruction that adapts recent advances in computer vision and object detection. Our architecture combines a Transformer hit filtering network with a MaskFormer reconstruction model that jointly optimises hit assignments and the estimation of the charged particles' properties. Evaluated on the TrackML dataset, our best performing model achieves state-of-the-art tracking performance with 97% efficiency for a fake rate of 0.6%, and inference times of 100ms. Our tunable approach enables specialisation for specific applications like triggering systems, while its underlying principles can be extended to other reconstruction challenges in high energy physics. This work demonstrates the potential of modern deep learning architectures to address emerging computational challenges in particle physics while maintaining the precision required for groundbreaking physics analysis.