An FPGA-based Trigger System with Online Track Recognition in COMET Phase-I

Yu Nakazawa, Yuki Fujii, Masahiro Ikeno, Yoshitaka Kuno, Myeong Jae Lee, Satoshi Mihara, Masayoshi Shoji, Tomohisa Uchida, Kazuki Ueno, Hisataka Yoshida

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)

Abstract

A field programmable gate array (FPGA)-based online trigger system has been developed for the COherent Muon To Electron Transition (COMET) Phase-I experiment, which searches for muon-to-electron conversion as a signature of New Physics beyond the Standard Model. A drift chamber and trigger counters are devised to detect a mono-energetic electron from the conversion process in a 1-T solenoidal magnetic field. A highly intense muon source enables reaching unprecedented experimental sensitivity, however, it also generates undesirable background particles which increase the trigger rate to a much higher level than the capability of the data acquisition (DAQ) system. By using hit information from the drift chamber in addition to the trigger counter, the newly proposed online trigger system efficiently suppresses the background trigger rate while keeping the signal-event acceptance large. A characteristic of this system is the utilization of machine learning (ML) techniques for optimizing lookup tables (LUTs) implemented in hardware. Simulation studies show that the signal-event acceptance of the online trigger is 96% while the background trigger rate is reduced from 91 to 13 kHz. The global trigger system and trigger electronics that construct a distributed trigger architecture were built. The total trigger latency was estimated to be 3.2 μs . The trigger system test was carried out by using a part of the drift-chamber readout region.

Original languageEnglish
Pages (from-to)2028-2034
Number of pages7
JournalIEEE Transactions on Nuclear Science
Volume68
Issue number8
DOIs
Publication statusPublished - 28 May 2021

Keywords

  • Data acquisition
  • Field programmable gate arrays
  • FPGA
  • Machine Learning
  • Mesons
  • Physics
  • Protons
  • Table lookup
  • Trigger system
  • Wires

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