The determination of charged particle trajectories in collisions at the CERN Large Hadron Collider (LHC) is an important but challenging problem, especially in the high interaction density conditions expected during the future high-luminosity phase of the LHC (HL-LHC). Graph neural networks (GNNs) are a type of geometric deep learning algorithm that has successfully been applied to this task by embedding tracker data as a graph -- nodes represent hits, while edges represent possible track segments -- and classifying the edges as true or fake track segments. However, their study in hardware- or software-based trigger applications has been limited due to their large computational cost. In this paper, we introduce an automated translation workflow, integrated into a broader tool called $\texttt{hls4ml}$, for converting GNNs into firmware for field-programmable gate arrays (FPGAs). We use this translation tool to implement GNNs for charged particle tracking, trained using the TrackML challenge dataset, on FPGAs with designs targeting different graph sizes, task complexites, and latency/throughput requirements. This work could enable the inclusion of charged particle tracking GNNs at the trigger level for HL-LHC experiments.

翻译：确定CERN 大型 Hadron相撞器(LHC)碰撞中的有电粒子轨迹是一个重要但具有挑战性的问题,特别是在LHC (HL-LHC) 未来高光度阶段预期的高交互密度条件下。 图形神经网络(GNNS)是一种几何深学习算法,已经成功地应用于这项任务,将跟踪器数据嵌入一个图表 -- -- 节点代表着撞击,而边缘代表着可能的轨道段 -- -- 并且将边缘分类为真实或假轨道段。然而,它们在硬件或软件触发应用程序中进行的研究有限,原因是其计算成本巨大。 在本文中,我们引入了一种自动翻译工作流程,将其整合到名为$\textt{hls4ml}的更广泛的工具中,用于将GNNNS转化为实地可规划的门阵列(FGGGGG)的硬件。我们使用这一翻译工具来实施GNNS的充电磁跟踪,并经过CrymL挑战数据集的培训,用于针对不同图形大小的设计、任务复杂度的磁带的磁带的磁带跟踪/透视系统。