基于全网状数据传输结构和联想记忆技术的带电粒子实时跟踪
Charged Particle Tracking in Real-Time Using a Full-Mesh Data Delivery Architecture and Associative Memory Techniques
摘要：
我们提出了一个灵活的和可扩展的方法来解决的挑战，带电粒子轨道重建的实时事件过滤器（1级触发器）在对撞机物理实验。这里描述的方法基于用于数据分布的全网格架构，并且依赖于关联存储器方法来实现模式识别算法，该模式识别算法快速识别和组织与源自粒子碰撞的粒子的轨迹相关联的命中。我们描述了一个成功的实施，由几个创新的硬件和算法元素的演示系统。全尺寸系统的实现依赖于这样的假设，即具有足够图案密度的关联存储器设备在未来通过专用ASIC或现代FPGA变得可用。我们表现出优异的性能方面的跟踪重建效率，纯度，动量分辨率，和处理时间测量的数据从一个模拟的LHC跟踪探测器。
英文摘要：
We present a flexible and scalable approach to address the challenges of charged particle track reconstruction in real-time event filters (Level-1 triggers) in collider physics experiments. The method described here is based on a full-mesh architecture for data distribution and relies on the Associative Memory approach to implement a pattern recognition algorithm that quickly identifies and organizes hits associated to trajectories of particles originating from particle collisions. We describe a successful implementation of a demonstration system composed of several innovative hardware and algorithmic elements. The implementation of a full-size system relies on the assumption that an Associative Memory device with the sufficient pattern density becomes available in the future, either through a dedicated ASIC or a modern FPGA. We demonstrate excellent performance in terms of track reconstruction efficiency, purity, momentum resolution, and processing time measured with data from a simulated LHC-like tracking detector.
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