标题： 基于异构图神经网络的可扩展多任务学习用于粒子碰撞事件重建 显示英文标题

标题： Scalable Multi-Task Learning for Particle Collision Event Reconstruction with Heterogeneous Graph Neural Networks

摘要： 大型强子对撞机的亮度前沿正在对粒子碰撞事件的重建和分析构成挑战。 增加的粒子多重性在数据采集阶段对延迟和存储需求造成了压力，同时出现了新的复杂情况，包括更高的背景水平和更频繁的粒子顶点误关联。 这进而需要开发更加全面和可扩展的重建方法，以利用机器学习的最新进展。 我们提出了一种新颖的异构图神经网络（HGNN）架构，该架构为不同的粒子碰撞关系提供了独特的表示，并集成了图剪枝层以实现可扩展性。 在模仿LHCb实验环境的多任务范式中进行训练，该HGNN显著提高了底夸克介子的重建性能。 值得注意的是，它在一个框架内同时执行粒子顶点关联和图剪枝。 我们量化了重建和剪枝性能，展示了事件复杂度增加时推理时间的增强扩展性，并通过加权消息传递方案减轻了潜在的性能损失。 显示英文摘要

摘要： The growing luminosity frontier at the Large Hadron Collider is challenging the reconstruction and analysis of particle collision events. Increased particle multiplicities are straining latency and storage requirements at the data acquisition stage, while new complications are emerging, including higher background levels and more frequent particle vertex misassociations. This in turn necessitates the development of more holistic and scalable reconstruction methods that take advantage of recent advances in machine learning. We propose a novel Heterogeneous Graph Neural Network (HGNN) architecture featuring unique representations for diverse particle collision relationships and integrated graph pruning layers for scalability. Trained with a multi-task paradigm in an environment mimicking the LHCb experiment, this HGNN significantly improves beauty hadron reconstruction performance. Notably, it concurrently performs particle vertex association and graph pruning within a single framework. We quantify reconstruction and pruning performance, demonstrate enhanced inference time scaling with event complexity, and mitigate potential performance loss using a weighted message passing scheme.