Combinatorial inverse problems in high energy physics span enormous algorithmic challenges. This work presents a new deep learning driven clustering algorithm that utilizes a space-time non-local trainable graph constructor, a graph neural network, and a set transformer. The model is trained with loss functions at the graph node, edge and object level, including contrastive learning and meta-supervision. The algorithm can be applied to problems such as charged particle tracking, calorimetry, pile-up discrimination, jet physics, and beyond. We showcase the effectiveness of this cutting-edge AI approach through particle tracking simulations. The code is available online.

翻译：高能物理中的组合逆问题涵盖了巨大的算法挑战。本文提出了一种基于深度学习的新型聚类算法，该算法利用时空非局域可训练图构造器、图神经网络和集合变换器。模型在图的节点、边和对象级别使用损失函数进行训练，包括对比学习和元监督。该算法可应用于带电粒子追踪、量热法、堆积鉴别、喷注物理等问题。我们通过粒子追踪模拟展示了这种前沿人工智能方法的有效性。代码已在线公开。