使用gpu的Mu3e在线事件选择

Valentin Henkys, B. Schmidt, N. Berger
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引用次数: 3

摘要

在寻找超越标准模型的物理过程中,Mu3e实验试图观察违反衰变μ+→e+e - e+的轻子风味。通过观察1•108μ/s的衰变产物,目的是观察这一过程,或者为其估计的分支比设定一个新的上限。高介子速率导致80gbps的高数据速率,主要是通过后台进程产生的数据。我们提出了在线事件选择,这是一种运行在12 Mu3e滤波器农场计算机的图形处理单元(GPU)上的三步算法。该算法首先采用简单快速的几何选择准则,将可能的候选事件数量减少到初始集合的5%以下;然后使用这些候选者重建完整的粒子轨道,正确重建超过97%的信号轨道。最后,使用简单的几何考虑来重建可能的衰减顶点,而不是完整的重建,正确识别超过94%的信号事件。我们还提出了该算法的完整实现,在目标μ子速率下满足所有性能要求,并成功地将数据速率降低了200倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Online Event Selection for Mu3e using GPUs
In the search for physics beyond the Standard Model the Mu3e experiment tries to observe the lepton flavor violating decay μ+ → e+e–e+. By observing the decay products of 1 • 108μ/s it aims to either observe the process, or set a new upper limit on its estimated branching ratio. The high muon rates result in high data rates of 80 Gbps, dominated by data produced through background processes. We present the Online Event Selection, a three step algorithm running on the graphics processing units (GPU) of the 12 Mu3e filter farm computers.By using simple and fast geometric selection criteria, the algorithm first reduces the amount of possible event candidates to below 5% of the initial set. These candidates are then used to reconstruct full particle tracks, correctly reconstructing over 97% of signal tracks. Finally a possible decay vertex is reconstructed using simple geometric considerations instead of a full reconstruction, correctly identifying over 94% of signal events.We also present a full implementation of the algorithm, fulfilling all performance requirements at the targeted muon rate and successfully reducing the data rate by a factor of 200.
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