A 3-D Track Reconstruction Algorithm for Preresearch of the STCF MDC L1 Trigger

IF 1.9 3区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Transactions on Nuclear Science Pub Date : 2024-11-20 DOI:10.1109/TNS.2024.3503068
Yidi Hao;Changqing Feng;Zixuan Zhou;Wenhao Dong;Zhujun Fang;Xueye Hu;Hang Zhou;Shubin Liu
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Abstract

The proposed Super Tau Charm Facility (STCF) is an electron-positron collider with high luminosity. Under the conditions of a high radiation, high counting-rate environment, and a pure physics event rate of up to 400 kHz, for a Level 1 (L1) trigger system, it is crucial to suppress background events to an acceptable rate. To this end, this study presents a 3-D track reconstruction algorithm for the preresearch of the STCF main drift chamber (MDC) L1 trigger. The proposed algorithm can reject tracks outside the interaction region and provide 3-D track information to the global trigger logic (GTL) of an L1 trigger for further analysis with other subdetectors. By using hit information from the MDC and the transverse momentum ( $p_{T}$ ) and azimuthal angle ( $\phi $ ) from previous research on 2-D reconstruction, this study reconstructs the z (longitudinal) position for the track vertex using a neural network-based method with an innovative stereo track segment (TS) design. The neural network is trained using high granularity quantization (HGQ) to reduce resource consumption while maintaining resolution. The proposed method is implemented into a field-programmable gate array (FPGA) using the hls4ml. The achieved resolution of the z-vertex reconstruction of a single track ( ${z}~\in $ [−50, 50] cm) is approximately 2.8 cm, which can ensure rejecting 97% beam background tracks in a $\pm 3\sigma $ interval.
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用于研究 STCF MDC L1 触发器的三维轨道重构算法
拟议的超级Tau魅力设施(STCF)是一个高亮度的正电子对撞机。在高辐射、高计数率环境和高达400khz的纯物理事件率条件下,对于1级(L1)触发系统来说,将背景事件抑制到可接受的速率是至关重要的。为此,本研究提出了一种三维航迹重建算法,用于STCF主漂移室(MDC) L1触发器的预研究。该算法可以拒绝交互区域外的轨迹,并向L1触发器的全局触发逻辑(GTL)提供三维轨迹信息,以便与其他子检测器进一步分析。本研究利用来自MDC的命中信息,以及之前二维重建研究中的横向动量($p_{T}$)和方位角($\phi $),利用创新的立体轨迹段(TS)设计,采用基于神经网络的方法重建轨迹顶点的z(纵向)位置。神经网络采用高粒度量化(high granularity quanti量化,HGQ)训练,在保持分辨率的同时减少资源消耗。该方法在现场可编程门阵列(FPGA)中使用hls4ml实现。单轨迹(${z}~\in $[−50,00050]cm)的z顶点重建分辨率约为2.8 cm,可以保证拒绝97% beam background tracks in a $\pm 3\sigma $ interval.
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来源期刊
IEEE Transactions on Nuclear Science
IEEE Transactions on Nuclear Science 工程技术-工程:电子与电气
CiteScore
3.70
自引率
27.80%
发文量
314
审稿时长
6.2 months
期刊介绍: The IEEE Transactions on Nuclear Science is a publication of the IEEE Nuclear and Plasma Sciences Society. It is viewed as the primary source of technical information in many of the areas it covers. As judged by JCR impact factor, TNS consistently ranks in the top five journals in the category of Nuclear Science & Technology. It has one of the higher immediacy indices, indicating that the information it publishes is viewed as timely, and has a relatively long citation half-life, indicating that the published information also is viewed as valuable for a number of years. The IEEE Transactions on Nuclear Science is published bimonthly. Its scope includes all aspects of the theory and application of nuclear science and engineering. It focuses on instrumentation for the detection and measurement of ionizing radiation; particle accelerators and their controls; nuclear medicine and its application; effects of radiation on materials, components, and systems; reactor instrumentation and controls; and measurement of radiation in space.
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Affiliate Plan of the IEEE Nuclear and Plasma Sciences Society IEEE Transactions on Nuclear Science Information for Authors Affiliate Plan of the IEEE Nuclear and Plasma Sciences Society Table of Contents IEEE Transactions on Nuclear Science Information for Authors
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