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arXiv 2310.08062hep-exphysics.ins-det
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在CMS Level-1触发器的Phase-2升级中使用种子锥算法重建喷注

Reconstructing jets in the Phase-2 upgrade of the CMS Level-1 Trigger with a seeded cone algorithm

Sioni Summers, Ioannis Bestintzanos, Giovanni Petrucciani

AI总结:

本文针对CMS Level-1触发器Phase-2升级,提出一种基于种子锥的喷注重建算法,在FPGA上实现低延迟、高吞吐的喷注识别,并支持神经网络喷注标记。

摘要:

CMS Level-1触发器(L1T)的Phase-2升级将使用粒子流算法重建粒子,连接来自径迹探测器、缪子探测器和量热计探测器的信息,并实现对喷注等高层次物理对象的细粒度重建。我们开发了一种喷注重建算法,使用以这些粒子流候选对象中高能种子为中心的锥体。该实现设计用于在每个Xilinx Ultrascale+ FPGA中查找最多16个喷注,延迟小于1微秒,事件吞吐量为6.7 MHz,以适应L1T系统约束。流水线处理使得能够以少量额外资源成本重建不同锥体尺寸的喷注集合。该算法的设计还为使用喷注成分进行额外计算提供了平台,例如使用神经网络进行喷注标记。我们将描述该实现、其喷注重建性能、计算指标以及喷注标记的发展方向。

英文摘要:

The Phase-2 Upgrade of the CMS Level-1 Trigger (L1T) will reconstruct particles using the Particle Flow algorithm, connecting information from the tracker, muon, and calorimeter detectors, and enabling fine-grained reconstruction of high level physics objects like jets. We have developed a jet reconstruction algorithm using a cone centred on an energetic seed from these Particle Flow candidates. The implementation is designed to find up to 16 jets in each Xilinx Ultrascale+ FPGA, with a latency of less than 1 μs, and event throughput of 6.7 MHz to fit within the L1T system constraints. Pipelined processing enables reconstruction of jet collections with different cone sizes for little additional resource cost. The design of the algorithm also provides a platform for additional computation using the jet constituents, such as jet tagging using neural networks. We will describe the implementation, its jet reconstruction performance, computational metrics, and the developments towards jet tagging.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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