Multi-particle reconstruction in the High Granularity Calorimeter using object condensation and graph neural networks Article Swipe
Shah Rukh Qasim
,
K. Long
,
J. Kieseler
,
M. Pierini
,
Raheel Nawaz
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1051/epjconf/202125103072
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1051/epjconf/202125103072
The high-luminosity upgrade of the LHC will come with unprecedented physics and computing challenges. One of these challenges is the accurate reconstruction of particles in events with up to 200 simultaneous protonproton interactions. The planned CMS High Granularity Calorimeter offers fine spatial resolution for this purpose, with more than 6 million channels, but also poses unique challenges to reconstruction algorithms aiming to reconstruct individual particle showers. In this contribution, we propose an end-to-end machine-learning method that performs clustering, classification, and energy and position regression in one step while staying within memory and computational constraints. We employ GravNet, a graph neural network, and an object condensation loss function to achieve this task. Additionally, we propose a method to relate truth showers to reconstructed showers by maximising the energy weighted intersection over union using maximal weight matching. Our results show the efficiency of our method and highlight a promising research direction to be investigated further.
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Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doaj.org/article/d48f5a1655a04727a9da5e9a36fd723e
- OA Status
- green
- Cited By
- 15
- References
- 9
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3194181657
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3194181657Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1051/epjconf/202125103072Digital Object Identifier
- Title
-
Multi-particle reconstruction in the High Granularity Calorimeter using object condensation and graph neural networksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-01Full publication date if available
- Authors
-
Shah Rukh Qasim, K. Long, J. Kieseler, M. Pierini, Raheel NawazList of authors in order
- Landing page
-
https://doaj.org/article/d48f5a1655a04727a9da5e9a36fd723ePublisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://doaj.org/article/d48f5a1655a04727a9da5e9a36fd723eDirect OA link when available
- Concepts
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Large Hadron Collider, Granularity, Cluster analysis, Computer science, Event reconstruction, Artificial neural network, Calorimeter (particle physics), Artificial intelligence, Particle physics, Graph, Object (grammar), Theoretical computer science, Physics, Operating system, Detector, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
15Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 3, 2023: 6, 2022: 4, 2021: 1Per-year citation counts (last 5 years)
- References (count)
-
9Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.6 | 49 |
| abstract_inverted_index.a | 97, 114, 145 |
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| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.8100000023841858 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.95998675 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |