Novel Approaches for ML-Assisted Particle Track Reconstruction and Hit Clustering

TL;DR

This paper explores innovative methods for particle track reconstruction in high-energy physics, including simplified data simulation, image segmentation networks, and Transformer models, aiming to improve accuracy and efficiency.

Contribution

It introduces novel approaches such as using simplified simulators and Transformer architectures for track reconstruction, which are new to the field.

Findings

Effective use of simplified training data for network architecture development

Potential of image segmentation networks for accurate track reconstruction

Preliminary success of Transformer models in hit sequence translation

Abstract

Track reconstruction is a vital aspect of High-Energy Physics (HEP) and plays a critical role in major experiments. In this study, we delve into unexplored avenues for particle track reconstruction and hit clustering. Firstly, we enhance the algorithmic design effort by utilising a simplified simulator (REDVID) to generate training data that is specifically composed for simplicity. We demonstrate the effectiveness of this data in guiding the development of optimal network architectures. Additionally, we investigate the application of image segmentation networks for this task, exploring their potential for accurate track reconstruction. Moreover, we approach the task from a different perspective by treating it as a hit sequence to track sequence translation problem. Specifically, we explore the utilisation of Transformer architectures for tracking purposes. Our preliminary findings are covered in detail. By considering this novel approach, we aim to uncover new insights and potential advancements in track reconstruction. This research sheds light on previously unexplored methods and provides valuable insights for the field of particle track reconstruction and hit clustering in HEP.