论文标题
通过FPGA上的机器学习带电的粒子跟踪
Charged Particle Tracking with Machine Learning on FPGAs
论文作者
论文摘要
在CERN大型强子对撞机（LHC）碰撞中的带电颗粒轨迹（跟踪）的测定是强子乘客事件重建最重要的方面之一。在LHC（HL-LHC）的未来高光度阶段预期的高条件下，尤其如此，其中每个梁交叉的相互作用数量将增加5倍。深度学习算法已成功应用于离线应用程序。但是，他们在基于硬件的触发应用程序中的研究受到限制。在本文中，我们研究了两个不同跟踪步骤的不同算法，并表明可以在现场可编程的门阵列（FPGA）上运行此类算法。
The determination of charged particle trajectories (tracking) in collisions at the CERN Large Hadron Collider (LHC) is one of the most important aspects for event reconstruction at hadron colliders. This is especially true in the high conditions expected during the future high-luminosity phase of the LHC (HL-LHC) where the number of interactions per beam crossing will increase by a factor of five. Deep learning algorithms have been successfully applied to this task for offline applications. However, their study in hardware-based trigger applications has been limited . In this paper, we study different algorithms for two different steps of tracking and show that such algorithms can be run on field-programmable gate arrays (FPGAs).
