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

• Published in: IEEE transactions on nuclear science Vol. 72; no. 3; pp. 429 - 437
• Main Authors: Hao, Yidi, Feng, Changqing, Zhou, Zixuan, Dong, Wenhao, Fang, Zhujun, Hu, Xueye, Zhou, Hang, Liu, Shubin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Approximation algorithms; Data acquisition; Detectors; Electron-positron accelerators; Field programmable gate arrays; Hls4ml; Level 1 (L1) trigger algorithm; Logic; Luminosity; main drift chamber (MDC); Monte Carlo methods; multilayer perceptron (MLP); neural network; Neural networks; Physics; Reconstruction; Reconstruction algorithms; Resource consumption; Super Tau Charm Facility (STCF); Three-dimensional displays; Transverse momentum; Wires
• ISSN: 0018-9499; 1558-1578
• DOI: 10.1109/TNS.2024.3503068

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 (<inline-formula> <tex-math notation="LaTeX">p_{T} </tex-math></inline-formula>) and azimuthal angle (<inline-formula> <tex-math notation="LaTeX">\phi </tex-math></inline-formula>) 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 (<inline-formula> <tex-math notation="LaTeX">{z}~\in </tex-math></inline-formula> [−50, 50] cm) is approximately 2.8 cm, which can ensure rejecting 97% beam background tracks in a <inline-formula> <tex-math notation="LaTeX">\pm 3\sigma </tex-math></inline-formula> interval.