Real-Time Charged Track Reconstruction for CLAS12
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910419142049792 |
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| author | Gavalian, Gagik |
| author_facet | Gavalian, Gagik |
| contents | This paper presents the results of charged particle track reconstruction in CLAS12 using artificial intelligence. In our approach, we use machine learning algorithms to reconstruct tracks, including their momentum and direction, with high accuracy from raw hits of the CLAS12 drift chambers. The reconstruction is performed in real-time, with the rate of data acquisition, and allows for the identification of event topologies in real-time. This approach revolutionizes the Nuclear Physics experiments' data processing, allowing us to identify and categorize the experimental data on the fly, and will lead to a significant reduction in experiment data processing. It can also be used in streaming readout applications leading to more efficient data acquisition and post-processing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_04020 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Real-Time Charged Track Reconstruction for CLAS12 Gavalian, Gagik Instrumentation and Detectors Nuclear Experiment Data Analysis, Statistics and Probability This paper presents the results of charged particle track reconstruction in CLAS12 using artificial intelligence. In our approach, we use machine learning algorithms to reconstruct tracks, including their momentum and direction, with high accuracy from raw hits of the CLAS12 drift chambers. The reconstruction is performed in real-time, with the rate of data acquisition, and allows for the identification of event topologies in real-time. This approach revolutionizes the Nuclear Physics experiments' data processing, allowing us to identify and categorize the experimental data on the fly, and will lead to a significant reduction in experiment data processing. It can also be used in streaming readout applications leading to more efficient data acquisition and post-processing. |
| title | Real-Time Charged Track Reconstruction for CLAS12 |
| topic | Instrumentation and Detectors Nuclear Experiment Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2403.04020 |