IMPROVEMENTS OF THE LOOT MODEL FOR PRIMARY VERTEX FINDING BASED ON THE ANALYSIS OF DEVELOPMENT RESULTS Article Swipe
E. Rezvaya
,
Pavel Goncharov
,
Y. Nefedov
,
G. Ososkov
,
A. Zhemchugov
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.54546/mlit.2021.76.95.001
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.54546/mlit.2021.76.95.001
The recognition of particle trajectories (tracks) from experimental measurements plays a key role in the reconstruction of events in experimental high-energy physics. Knowledge about the primary vertex of an event can significantly improve the quality of track reconstruction. To solve the problem of primary vertex finding in the BESIII inner tracking detector we applied the LOOT program which is a deep convolutional neural network that processes all event hits at once, like a three-dimensional image. We used mean absolute error to measure the quality of the trained model, but a thorough analysis of the results showed that this metric by itself is inadequate without considering output distributions of the vertex coordinates. Correcting all errors allowed us to propose special corrections to the loss function that gave quite acceptable results. The process of our problem investigation and itsoutcomes are presented.
Related Topics
Concepts
Vertex (graph theory)
Convolutional neural network
Event (particle physics)
Computer science
Artificial intelligence
Metric (unit)
Detector
Algorithm
Pattern recognition (psychology)
Mathematics
Theoretical computer science
Physics
Graph
Telecommunications
Operations management
Economics
Quantum mechanics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.54546/mlit.2021.76.95.001
- https://doi.org/10.54546/mlit.2021.76.95.001
- OA Status
- hybrid
- References
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4205635586
All OpenAlex metadata
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https://openalex.org/W4205635586Canonical identifier for this work in OpenAlex
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https://doi.org/10.54546/mlit.2021.76.95.001Digital Object Identifier
- Title
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IMPROVEMENTS OF THE LOOT MODEL FOR PRIMARY VERTEX FINDING BASED ON THE ANALYSIS OF DEVELOPMENT RESULTSWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
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2021Year of publication
- Publication date
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2021-12-13Full publication date if available
- Authors
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E. Rezvaya, Pavel Goncharov, Y. Nefedov, G. Ososkov, A. ZhemchugovList of authors in order
- Landing page
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https://doi.org/10.54546/mlit.2021.76.95.001Publisher landing page
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https://doi.org/10.54546/mlit.2021.76.95.001Direct link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.54546/mlit.2021.76.95.001Direct OA link when available
- Concepts
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Vertex (graph theory), Convolutional neural network, Event (particle physics), Computer science, Artificial intelligence, Metric (unit), Detector, Algorithm, Pattern recognition (psychology), Mathematics, Theoretical computer science, Physics, Graph, Telecommunications, Operations management, Economics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.by | 99 |
| abstract_inverted_index.in | 13, 18, 46 |
| abstract_inverted_index.is | 58, 101 |
| abstract_inverted_index.of | 2, 16, 27, 35, 42, 84, 92, 107, 131 |
| abstract_inverted_index.to | 80, 116, 120 |
| abstract_inverted_index.us | 115 |
| abstract_inverted_index.we | 52 |
| abstract_inverted_index.The | 0, 129 |
| abstract_inverted_index.all | 66, 112 |
| abstract_inverted_index.and | 135 |
| abstract_inverted_index.are | 137 |
| abstract_inverted_index.but | 88 |
| abstract_inverted_index.can | 30 |
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| abstract_inverted_index.the | 14, 24, 33, 40, 47, 54, 82, 85, 93, 108, 121 |
| abstract_inverted_index.LOOT | 55 |
| abstract_inverted_index.deep | 60 |
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| abstract_inverted_index.hits | 68 |
| abstract_inverted_index.like | 71 |
| abstract_inverted_index.loss | 122 |
| abstract_inverted_index.mean | 77 |
| abstract_inverted_index.role | 12 |
| abstract_inverted_index.that | 64, 96, 124 |
| abstract_inverted_index.this | 97 |
| abstract_inverted_index.used | 76 |
| abstract_inverted_index.about | 23 |
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| abstract_inverted_index.event | 29, 67 |
| abstract_inverted_index.inner | 49 |
| abstract_inverted_index.once, | 70 |
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| abstract_inverted_index.solve | 39 |
| abstract_inverted_index.track | 36 |
| abstract_inverted_index.which | 57 |
| abstract_inverted_index.BESIII | 48 |
| abstract_inverted_index.errors | 113 |
| abstract_inverted_index.events | 17 |
| abstract_inverted_index.image. | 74 |
| abstract_inverted_index.itself | 100 |
| abstract_inverted_index.metric | 98 |
| abstract_inverted_index.model, | 87 |
| abstract_inverted_index.neural | 62 |
| abstract_inverted_index.output | 105 |
| abstract_inverted_index.showed | 95 |
| abstract_inverted_index.vertex | 26, 44, 109 |
| abstract_inverted_index.allowed | 114 |
| abstract_inverted_index.applied | 53 |
| abstract_inverted_index.finding | 45 |
| abstract_inverted_index.improve | 32 |
| abstract_inverted_index.measure | 81 |
| abstract_inverted_index.network | 63 |
| abstract_inverted_index.primary | 25, 43 |
| abstract_inverted_index.problem | 41, 133 |
| abstract_inverted_index.process | 130 |
| abstract_inverted_index.program | 56 |
| abstract_inverted_index.propose | 117 |
| abstract_inverted_index.quality | 34, 83 |
| abstract_inverted_index.results | 94 |
| abstract_inverted_index.special | 118 |
| abstract_inverted_index.trained | 86 |
| abstract_inverted_index.without | 103 |
| abstract_inverted_index.(tracks) | 5 |
| abstract_inverted_index.absolute | 78 |
| abstract_inverted_index.analysis | 91 |
| abstract_inverted_index.detector | 51 |
| abstract_inverted_index.function | 123 |
| abstract_inverted_index.particle | 3 |
| abstract_inverted_index.physics. | 21 |
| abstract_inverted_index.results. | 128 |
| abstract_inverted_index.thorough | 90 |
| abstract_inverted_index.tracking | 50 |
| abstract_inverted_index.Knowledge | 22 |
| abstract_inverted_index.processes | 65 |
| abstract_inverted_index.Correcting | 111 |
| abstract_inverted_index.acceptable | 127 |
| abstract_inverted_index.inadequate | 102 |
| abstract_inverted_index.presented. | 138 |
| abstract_inverted_index.considering | 104 |
| abstract_inverted_index.corrections | 119 |
| abstract_inverted_index.high-energy | 20 |
| abstract_inverted_index.itsoutcomes | 136 |
| abstract_inverted_index.recognition | 1 |
| abstract_inverted_index.coordinates. | 110 |
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| abstract_inverted_index.trajectories | 4 |
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| abstract_inverted_index.distributions | 106 |
| abstract_inverted_index.investigation | 134 |
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| abstract_inverted_index.reconstruction | 15 |
| abstract_inverted_index.reconstruction. | 37 |
| abstract_inverted_index.three-dimensional | 73 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5074201549 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I32699454 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.7300000190734863 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.18306636 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |