Speaker
Description
Online reconstruction plays a crucial role in monitoring and real-time analysis of High Energy and Nuclear Physics experiments. A vital aspect of reconstruction algorithms is particle identification (PID), which combines information from various detector components to determine a particle's type. Electron identification is particularly significant in electro-production Nuclear Physics experiments like CLAS12 as it triggers data recording. A machine learning approach has been developed for CLAS12 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 speeds, enabling real-time electron reconstruction. This advancement significantly improves online analyses and monitoring capabilities for CLAS12.
Significance
We aim to attain full online reconstruction at CLAS12, allowing for in real time analysis during data taking. This is a novel paradigm in nuclear physics experiments that will be impactful in triggering, online event selection and monitoring. This presentation will present new results from the online implementation and validation that have not been shown before.
References
CHEP2024 Presentation: https://indico.cern.ch/event/1338689/contributions/6015442/
Previous work on machine learning electron trigger at CLAS12:
R. Tyson, G. Gavalian, D.G. Ireland, B. McKinnon, Deep learning level-3 electron trigger for CLAS12, Comp. Phys. Comm. 290, 108783 (2023).
| Experiment context, if any | The data used for this work has been produced by the CLAS12 experiment located at the Thomas Jefferson National Accelerator Facility. |
|---|
