Physics Performance of the ATLAS GNN4ITk Track Reconstruction Chain Article Swipe
Sylvain Caillou
,
P. Calafiura
,
X. Ju
,
Daniel Murnane
,
Tuan Q. Pham
,
C. Rougier
,
Jan Stark
,
A. Vallier
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1051/epjconf/202429503030
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1051/epjconf/202429503030
Particle tracking is vital for the ATLAS physics programs. To cope with the increased number of particles in the High Luminosity LHC, ATLAS is building a new all-silicon Inner Tracker (ITk), consisting of a Pixel and a Strip subdetector. At the same time, ATLAS is developing new track reconstruction algorithms that can operate in the HL-LHC dense environment. A track reconstruction algorithm needs to solve two problems: track finding for building track candidates and track fitting for obtaining track parameters of those track candidates. Previously, we developed GNN4ITk, a track-finding algorithm based on a Graph Neural Network (GNN), and achieved good track-finding performance under realistic HL-LHC conditions. Our GNN pipeline relied only on the 3D spacepoint positions. This work introduces heterogeneous GNN models to fully exploit the subdetector-dependent features of ITk data, improving the performance of our GNN4ITk pipeline. In addition, we interfaced our pipeline to the standard ATLAS track-fitting algorithm and data model. With that, the GNN4ITk pipeline produces full-fledged track candidates that can be used for any downstream analyses and compared with the other track reconstruction algorithms.
Related Topics
Concepts
Large Hadron Collider
Atlas (anatomy)
Track (disk drive)
ATLAS experiment
Pipeline (software)
Tracking (education)
Computer science
Algorithm
Real-time computing
Artificial intelligence
Computer vision
Physics
Particle physics
Operating system
Programming language
Pedagogy
Psychology
Paleontology
Biology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1051/epjconf/202429503030
- https://www.epj-conferences.org/articles/epjconf/pdf/2024/05/epjconf_chep2024_03030.pdf
- OA Status
- diamond
- Cited By
- 7
- References
- 3
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396662081
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4396662081Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1051/epjconf/202429503030Digital Object Identifier
- Title
-
Physics Performance of the ATLAS GNN4ITk Track Reconstruction ChainWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-01Full publication date if available
- Authors
-
Sylvain Caillou, P. Calafiura, X. Ju, Daniel Murnane, Tuan Q. Pham, C. Rougier, Jan Stark, A. VallierList of authors in order
- Landing page
-
https://doi.org/10.1051/epjconf/202429503030Publisher landing page
- PDF URL
-
https://www.epj-conferences.org/articles/epjconf/pdf/2024/05/epjconf_chep2024_03030.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://www.epj-conferences.org/articles/epjconf/pdf/2024/05/epjconf_chep2024_03030.pdfDirect OA link when available
- Concepts
-
Large Hadron Collider, Atlas (anatomy), Track (disk drive), ATLAS experiment, Pipeline (software), Tracking (education), Computer science, Algorithm, Real-time computing, Artificial intelligence, Computer vision, Physics, Particle physics, Operating system, Programming language, Pedagogy, Psychology, Paleontology, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 6, 2024: 1Per-year citation counts (last 5 years)
- References (count)
-
3Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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