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

TL;DR

This work addresses the compute-intensive challenge of track reconstruction in collider experiments by proposing a complexity-driven ML-design workflow and utilizing a simplified data generator, REDVID, to rapidly prototype architectures. It evaluates four ML approaches—three Transformer-based models (EncDec, EncCla, EncReg) and a U-Net segmentation model—for hit clustering and hit-to-track translation, with distinct input-output formulations and post-processing steps. The study demonstrates that these models achieve competitive performance on synthetic helically-tracked data and highlights the benefits of modular, ML-assisted pipelines over monolithic solutions. The proposed methodology enables rapid design-space exploration and provides a foundation for integrating ML into scalable, real-world HEP tracking workflows, pending validation on physics-accurate simulations and experimental data.

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.

Figures (2)

• Figure 1: Methodology framework
• Figure 2: Depicting the high-level views of dedicated workflows for each ML model design.