Online Machine-Learning-Based Event Selection for COMET Phase-I Article Swipe
Yuki Fujii
,
Masaki Miyataki
,
M. J. Lee
,
Y. Nakazawa
,
Liam Pinchbeck
,
K. Ueno
,
Hisataka Yoshida
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/psf2023008032
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/psf2023008032
In many modern particle physics experiments, high-rate data handling is one of the most critical challenges due to the increase in particle intensity required to achieve higher statistics. We will tackle the challenge in the COMET experiment by developing the sub-microseconds ultra-fast machine learning (ML) algorithm implemented inside FPGAs to search for the lepton flavour violation process, a μ-e conversion, using the world’s most intense muon beam. Our previous study showed that a trigger algorithm based on a gradient-boosted decision tree will realise the sufficient trigger performance within 3.2 μs with a cut-based event classification. In this paper, we further investigated neural network algorithms as event classifications. For the feasibility test, a multi-layer perceptron (MLP) model was implemented inside the FPGA, and the preliminary results are presented.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/psf2023008032
- https://www.mdpi.com/2673-9984/8/1/32/pdf?version=1691055355
- OA Status
- gold
- Cited By
- 1
- References
- 10
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385546848
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385546848Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/psf2023008032Digital Object Identifier
- Title
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Online Machine-Learning-Based Event Selection for COMET Phase-IWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-08-03Full publication date if available
- Authors
-
Yuki Fujii, Masaki Miyataki, M. J. Lee, Y. Nakazawa, Liam Pinchbeck, K. Ueno, Hisataka YoshidaList of authors in order
- Landing page
-
https://doi.org/10.3390/psf2023008032Publisher landing page
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https://www.mdpi.com/2673-9984/8/1/32/pdf?version=1691055355Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2673-9984/8/1/32/pdf?version=1691055355Direct OA link when available
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Comet, Computer science, Selection (genetic algorithm), Event (particle physics), Artificial intelligence, Astrobiology, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2023: 1Per-year citation counts (last 5 years)
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10Number of works referenced by this work
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-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.one | 10 |
| abstract_inverted_index.the | 12, 18, 31, 34, 39, 52, 61, 83, 108, 119, 122 |
| abstract_inverted_index.was | 116 |
| abstract_inverted_index.(ML) | 44 |
| abstract_inverted_index.data | 7 |
| abstract_inverted_index.many | 1 |
| abstract_inverted_index.most | 13, 63 |
| abstract_inverted_index.muon | 65 |
| abstract_inverted_index.that | 71 |
| abstract_inverted_index.this | 96 |
| abstract_inverted_index.tree | 80 |
| abstract_inverted_index.will | 29, 81 |
| abstract_inverted_index.with | 90 |
| abstract_inverted_index.(MLP) | 114 |
| abstract_inverted_index.COMET | 35 |
| abstract_inverted_index.FPGA, | 120 |
| abstract_inverted_index.FPGAs | 48 |
| abstract_inverted_index.based | 75 |
| abstract_inverted_index.beam. | 66 |
| abstract_inverted_index.event | 93, 105 |
| abstract_inverted_index.model | 115 |
| abstract_inverted_index.study | 69 |
| abstract_inverted_index.test, | 110 |
| abstract_inverted_index.using | 60 |
| abstract_inverted_index.higher | 26 |
| abstract_inverted_index.inside | 47, 118 |
| abstract_inverted_index.lepton | 53 |
| abstract_inverted_index.modern | 2 |
| abstract_inverted_index.neural | 101 |
| abstract_inverted_index.paper, | 97 |
| abstract_inverted_index.search | 50 |
| abstract_inverted_index.showed | 70 |
| abstract_inverted_index.tackle | 30 |
| abstract_inverted_index.within | 87 |
| abstract_inverted_index.achieve | 25 |
| abstract_inverted_index.flavour | 54 |
| abstract_inverted_index.further | 99 |
| abstract_inverted_index.intense | 64 |
| abstract_inverted_index.machine | 42 |
| abstract_inverted_index.network | 102 |
| abstract_inverted_index.physics | 4 |
| abstract_inverted_index.realise | 82 |
| abstract_inverted_index.results | 124 |
| abstract_inverted_index.trigger | 73, 85 |
| abstract_inverted_index.critical | 14 |
| abstract_inverted_index.decision | 79 |
| abstract_inverted_index.handling | 8 |
| abstract_inverted_index.increase | 19 |
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| abstract_inverted_index.particle | 3, 21 |
| abstract_inverted_index.previous | 68 |
| abstract_inverted_index.process, | 56 |
| abstract_inverted_index.required | 23 |
| abstract_inverted_index.μs | 89 |
| abstract_inverted_index.algorithm | 45, 74 |
| abstract_inverted_index.challenge | 32 |
| abstract_inverted_index.cut-based | 92 |
| abstract_inverted_index.high-rate | 6 |
| abstract_inverted_index.intensity | 22 |
| abstract_inverted_index.violation | 55 |
| abstract_inverted_index.μ-e | 58 |
| abstract_inverted_index.algorithms | 103 |
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| abstract_inverted_index.experiment | 36 |
| abstract_inverted_index.perceptron | 113 |
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| abstract_inverted_index.conversion, | 59 |
| abstract_inverted_index.feasibility | 109 |
| abstract_inverted_index.implemented | 46, 117 |
| abstract_inverted_index.multi-layer | 112 |
| abstract_inverted_index.performance | 86 |
| abstract_inverted_index.preliminary | 123 |
| abstract_inverted_index.statistics. | 27 |
| abstract_inverted_index.experiments, | 5 |
| abstract_inverted_index.investigated | 100 |
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| abstract_inverted_index.classifications. | 106 |
| abstract_inverted_index.gradient-boosted | 78 |
| abstract_inverted_index.sub-microseconds | 40 |
| abstract_inverted_index.world’s | 62 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5002773688 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 7 |
| corresponding_institution_ids | https://openalex.org/I56590836 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.4699999988079071 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.49075583 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |