Scholarlink

<p>The Jiangmen Underground Neutrino Observatory (JUNO) is a multipurpose detector with a rich physics program, including, among others, neutrino oscillation, geo-neutrinos, and astrophysical neutrinos. To meet the demands of measuring the solar <i>pp</i> neutrinos, an online <i>pp</i> event processing flow is designed in the JUNO data acquisition system (DAQ). The JUNO DAQ will identify and store solar <i>pp</i> neutrino candidates from the FPGA T/Q data readout from the front end through the online <i>pp</i> event processing flow. The processing flow first conducts a level-one software trigger to filter out most of the dark count events caused by photomultiplier tubes (PMTs). Then, the online processing flow executes more complex processing algorithms to improve the signal-to-noise ratio of <i>pp</i> neutrino events. To handle the nearly 18000 front-end electronics channels, each with a 30 kHz PMT dark count rate, the level-one software trigger must process vast amounts of data. This imposes stringent requirements on the performance and real-time capabilities of the trigger algorithm. Currently, the multiplicity trigger algorithm serves as an effective alternative applicable to level-one software trigger, capable of meeting the experimental demands for reducing dark count events. This paper presents a study on the software implementation and runtime latency of the multiplicity trigger algorithm and proposes an improved implementation method that can further reduce its processing latency during online processing.</p>

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A multiplicity software trigger algorithm for JUNO

  • 期刊论文

  • 发表日期：

    2025年5月26日

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  • Yu Peng,
  • Xiaolu Ji,
  • Kejun Zhu,
  • Shuihan Zhang,
  • Fei Li,
  • Yongpeng Zhang,
  • Yixiang Yang,
  • Tingxuan Zeng

摘要

The Jiangmen Underground Neutrino Observatory (JUNO) is a multipurpose detector with a rich physics program, including, among others, neutrino oscillation, geo-neutrinos, and astrophysical neutrinos. To meet the demands of measuring the solar pp neutrinos, an online pp event processing flow is designed in the JUNO data acquisition system (DAQ). The JUNO DAQ will identify and store solar pp neutrino candidates from the FPGA T/Q data readout from the front end through the online pp event processing flow. The processing flow first conducts a level-one software trigger to filter out most of the dark count events caused by photomultiplier tubes (PMTs). Then, the online processing flow executes more complex processing algorithms to improve the signal-to-noise ratio of pp neutrino events. To handle the nearly 18000 front-end electronics channels, each with a 30 kHz PMT dark count rate, the level-one software trigger must process vast amounts of data. This imposes stringent requirements on the performance and real-time capabilities of the trigger algorithm. Currently, the multiplicity trigger algorithm serves as an effective alternative applicable to level-one software trigger, capable of meeting the experimental demands for reducing dark count events. This paper presents a study on the software implementation and runtime latency of the multiplicity trigger algorithm and proposes an improved implementation method that can further reduce its processing latency during online processing.