What question did this study set out to answer?

This research aims to enhance real-time electron identification in high energy physics experiments using machine learning techniques.

February 5, 2026

Online Electron Reconstruction at CLAS12

Key Points

This research aims to enhance real-time electron identification in high energy physics experiments using machine learning techniques.
Developed machine learning algorithms for electron reconstruction
Combined raw signals from multiple detector components
Implemented algorithms at the data acquisition level
Achieved high electron identification purity
Maintained nearly 100% efficiency in identification
Machine learning tools exceeded data acquisition speeds for real-time processing

Abstract

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.

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