Innovations in trigger technology for future advanced accelerator experiments with "AI Trigger"
| Project/Area Number |
18K03675
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 15020:Experimental studies related to particle-, nuclear-, cosmic ray and astro-physics
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| Research Institution | High Energy Accelerator Research Organization (2020-2021)
Nagoya University (2018-2019) |
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Principal Investigator |
Tomoto Makoto 大学共同利用機関法人高エネルギー加速器研究機構, 素粒子原子核研究所, 教授 (80432235)
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| Project Period (FY) |
2018-04-01 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2020: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2019: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2018: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | Trigger / LHC/ATLAS実験 / エネルギーフロンティア実験 / トリガー / 機械学習 / 素粒子実験 |
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| Outline of Final Research Achievements |
We have developed the new trigger technique to remove more effectively background events that increase along with interesting physics events in future energy frontier experiments such as the high-luminosity LHC experiment. A prototype of a general-purpose trigger board carrying a high-end FPGA and high-speed and multi-input/output optical transceivers was produced. The "Track Fit Trigger" algorithm for the muon trigger, which will be introduced to the high-luminosity LHC experiment, was completed and implemented on the prototype trigger board. An "AI trigger" algorithm incorporating Convolutional Neural Networks (CNN) and other technologies was devised to aim to replace the "Track Fit Trigger", and the feasibility of implementing this algorithm in the future energy frontier experiments was explored.
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| Academic Significance and Societal Importance of the Research Achievements |
高輝度LHC実験において最初に導入するミューオントリガーアルゴリズムとして"Track Fit Trigger"を使って物理データを収集することが国際共同研究者から認められた。さらに、トリガーボードを完成させ量産準備に取り掛かることができた。単純な検出器セットアップによる検証ではあるが、「AIトリガー」が"Track Fit Trigger"以上の性能を出せる可能性を持つことを立証し、学術論文としてまとめ投稿した（論文は承認待ち中）。また、このアルゴリズムは、ミューオントリガーだけでなく、シリコン検出器などを用いて実現する荷電粒子飛跡トリガーへの波及効果など応用性があることが認められた。
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Report
(5 results)
Research Products
(23 results)
