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。

粒子跟踪语言模型

A Language Model for Particle Tracking

Particle tracking is a fundamental task in digital microscopy. Recently,
machine-learning approaches have made great strides in overcoming the
limitations of more classical approaches. The training of state-of-the-art
machine-learning methods almost universally relies on either vast amounts of
labeled experimental data or the ability to numerically simulate realistic
datasets. However, the data produced by experiments are often challenging to
label and cannot be easily reproduced numerically. Here, we propose a novel
deep-learning method, named LodeSTAR (Low-shot deep Symmetric Tracking And
Regression), that learns to tracks objects with sub-pixel accuracy from a
single unlabeled experimental image. This is made possible by exploiting the
inherent roto-translational symmetries of the data. We demonstrate that
LodeSTAR outperforms traditional methods in terms of accuracy. Furthermore, we
analyze challenging experimental data containing densely packed cells or noisy
backgrounds. We also exploit additional symmetries to extend the measurable
particle properties to the particle's vertical position by propagating the
signal in Fourier space and its polarizability by scaling the signal strength.
Thanks to the ability to train deep-learning models with a single unlabeled
image, LodeSTAR can accelerate the development of high-quality microscopic
analysis pipelines for engineering, biology, and medicine.

提出了一种名为 LodeSTAR 的新型深度学习方法，可以在单个未标记的实验图像中学习以亚像素级精度跟踪对象。 该算法利用了数据的旋转平移对称性，并扩展了可测量的粒子属性，可以加速高质量的微观分析流水线的发展。

单镜头自监督粒子跟踪

Single-shot self-supervised particle tracking

This paper reports the results of an experiment in high energy physics: using
the power of the "crowd" to solve difficult experimental problems linked to
tracking accurately the trajectory of particles in the Large Hadron Collider
(LHC). This experiment took the form of a machine learning challenge organized
in 2018: the Tracking Machine Learning Challenge (TrackML). Its results were
discussed at the competition session at the Neural Information Processing
Systems conference (NeurIPS 2018). Given 100.000 points, the participants had
to connect them into about 10.000 arcs of circles, following the trajectory of
particles issued from very high energy proton collisions. The competition was
difficult with a dozen front-runners well ahead of a pack. The single
competition score is shown to be accurate and effective in selecting the best
algorithms from the domain point of view. The competition has exposed a
diversity of approaches, with various roles for Machine Learning, a number of
which are discussed in the document

采用众包和机器学习等方法，解决粒子轨迹跟踪等高能物理实验中的难题，并在跟踪机器学习挑战赛中得出了一些算法结果。