Particle tracking is crucial for almost all physics analysis programs at the
Large Hadron Collider. Deep learning models are pervasively used in particle
tracking related tasks. However, the current practice is to design and train
one deep learning model for one task with supervised learning techniques. The
trained models work well for tasks they are trained on but show no or little
generalization capabilities. We propose to unify these models with a language
model. In this paper, we present a tokenized detector representation that
allows us to train a BERT model for particle tracking. The trained BERT model,
namely TrackingBERT, offers latent detector module embedding that can be used
for other tasks. This work represents the first step towards developing a
foundational model for particle detector understanding.

粒子跟踪是大型强子对撞机几乎所有物理分析程序的关键，深度学习模型广泛应用于与粒子跟踪相关的任务。然而，目前的做法是为一个任务设计和训练一个深度学习模型，采用有监督学习技术。训练的模型对于训练过的任务效果良好，但在泛化能力方面显示出很少或没有。我们提出用语言模型统一这些模型。本文提出了一种标记化探测器表示法，可以用来训练粒子跟踪的 BERT 模型。训练的 BERT 模型，即 TrackingBERT，提供了可以用于其他任务的潜在探测器模块嵌入。这项工作代表了为粒子探测器理解开发基础模型的第一步。