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.

翻译：径迹重建是高能物理（HEP）领域的关键环节，在大型实验中发挥着至关重要的作用。本研究深入探索了粒子径迹重建与击中聚类的若干新途径。首先，我们通过使用简化模拟器（REDVID）生成专为简化而设计的训练数据，以增强算法设计工作。我们证明了此类数据在指导最优网络架构开发方面的有效性。此外，我们研究了图像分割网络在此任务中的应用，探索了其在精确径迹重建方面的潜力。更进一步，我们从不同视角切入，将该任务视为击中序列到径迹序列的翻译问题。具体而言，我们探索了将Transformer架构用于径迹重建的可行性。我们的初步研究成果已在文中详述。通过这一新颖的研究思路，我们旨在为径迹重建领域揭示新的见解与潜在进展。本研究揭示了此前未经探索的方法，为HEP中的粒子径迹重建与击中聚类领域提供了有价值的参考。