Deep learning for track recognition in pixel and strip-based particle detectors Article Swipe
O. Bakina
,
D. Baranov
,
I. Denisenko
,
P. Goncharov
,
Andrey Nechaevskiy
,
Yu. Nefedov
,
A. Nikolskaya
,
G. Ososkov
,
D. Rusov
,
E. Shchavelev
,
Shaodong Sun
,
Le-Tian Wang
,
Yuyue Zhang
,
A. Zhemchugov
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2210.00599
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2210.00599
The reconstruction of charged particle trajectories in tracking detectors is a key problem in the analysis of experimental data for high-energy and nuclear physics. The amount of data in modern experiments is so large that classical tracking methods, such as the Kalman filter, cannot process them fast enough. To solve this problem, we developed two neural network algorithms based on deep learning architectures for track recognition in pixel and strip-based particle detectors. These are TrackNETv3 for local (track by track) and RDGraphNet for global (all tracks in an event) tracking. These algorithms were tested using the GEM tracker of the BM@N experiment at JINR (Dubna) and the cylindrical GEM inner tracker of the BESIII experiment at IHEP CAS (Beijing). The RDGraphNet model, based on a reverse directed graph, showed encouraging results: 95% recall and 74% precision for track finding. The TrackNETv3 model demonstrated a recall value of 95% and 76% precision. This result can be improved after further model optimization.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2210.00599
- https://arxiv.org/pdf/2210.00599
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4302011581
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4302011581Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2210.00599Digital Object Identifier
- Title
-
Deep learning for track recognition in pixel and strip-based particle detectorsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-10-02Full publication date if available
- Authors
-
O. Bakina, D. Baranov, I. Denisenko, P. Goncharov, Andrey Nechaevskiy, Yu. Nefedov, A. Nikolskaya, G. Ososkov, D. Rusov, E. Shchavelev, Shaodong Sun, Le-Tian Wang, Yuyue Zhang, A. ZhemchugovList of authors in order
- Landing page
-
https://arxiv.org/abs/2210.00599Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2210.00599Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2210.00599Direct OA link when available
- Concepts
-
Tracking (education), Track (disk drive), Artificial intelligence, Detector, Particle filter, Computer science, Kalman filter, Pixel, Artificial neural network, Graph, Computer vision, Deep learning, Process (computing), Theoretical computer science, Psychology, Telecommunications, Pedagogy, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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