论文标题
在枪管形和高磁场中的高轨多重性的迭代视网膜
Iterative Retina for high track multiplicity in a barrel-shape tracker and high magnetic field
论文作者
论文摘要
在高亮度运行的山脉中，高能量物理实验中的实时跟踪对于触发系统非常具有挑战性。为了在在线触发系统中执行模式识别和轨道拟合，该领域已经引入了人工视网膜算法。可以在最先进的FPGA设备的状态下实施视网膜。我们的发展以一种迭代方式使用视网膜来识别嵌入在高磁场和高轨道多样性中的桶形跟踪器的轨道。作为基准，我们模拟了LHC T-TBAR事件，堆积为200，以及基于Geant-4的模拟，对硅模块制成的6层枪管跟踪器探测器。使用此示例，评估了硬件设计（资源使用，延迟）的性能。视网膜拟合的效率和纯度均超过90％。此外，在视网膜拟合后，我们还添加了Kalman滤波器，以改善轨道参数的分辨率。我们的仿真结果表明，Kalman滤波器可以与视网膜算法结合使用，以通过TBAR事件找到轨道，并提供重建参数的高分辨率。
Real-time track tracking in high energy physics experiments at colliders running at high luminosity is very challenging for trigger systems. To perform pattern-recognition and track fitting in online trigger system, the artificial Retina algorithm has been introduced in the field. Retina can be implemented in the state of the art FPGA devices. Our developments use Retina in an iterative way to identify track for barrel-shape tracker embedded in a high magnetic field and with high track multiplicity. As a benchmark we simulate LHC t-tbar events, with a pile-up of 200 and a GEANT-4 based simulation of a 6-layers barrel tracker detector made of silicon modules. With this sample the performance of the hardware design (resource usage, latency) is evaluated. Both efficiency and purity of the Retina fitting are over 90%. Moreover we have also added a Kalman filter after the Retina fit to improve the resolution on the track parameters. Our simulation results show that the Kalman filter can work well together with the Retina algorithm to find track through t-tbar event and provides high resolutions of the reconstructed parameters.
