| Issue |
EPJ Web Conf.
Volume 337, 2025
27th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2024)
|
|
|---|---|---|
| Article Number | 01016 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/epjconf/202533701016 | |
| Published online | 07 October 2025 | |
https://doi.org/10.1051/epjconf/202533701016
Online Electron Reconstruction at CLAS12
Thomas Jefferson National Accelerator Facility, Newport News, VA 23606, USA
* e-mail: tyson@jlab.org
** e-mail: gavalian@jlab.org
Published online: 7 October 2025
Online reconstruction plays a crucial role in monitoring and in real-time analysis of high energy and nuclear physics experiments. A vital aspect of reconstruction algorithms is particle identification, which combines information from various detector components to determine the type of particle. Electron identification is particularly significant in electro-production nuclear physics experiments like the CLAS12 spectrometer at Jefferson Laboratory as it is essential in data recording. A machine learning approach has been developed for CLAS12 experiments to reconstruct and identify electrons by combining raw signals from multiple detector components at the data acquisition level. This method achieves high electron identification purity while maintaining nearly 100% efficiency. Furthermore, the machine learning tools operate at rates exceeding data acquisition speed, enabling the real-time electron reconstruction. This advancement significantly improves online analyses and monitoring capabilities for CLAS12 experiments.
© The Authors, published by EDP Sciences, 2025
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