PoS - Proceedings of Science
Volume 456 - 25th International Symposium on Spin Physics (SPIN2023) - Future Facilities and Experiments
Utilizing Machine Learning Pattern Recognition for Online Monitoring and Visualization in SpinQuest Experiment
J. Roberts* and D. Keller
Full text: pdf
Published on: July 30, 2024
Abstract
SpinQuest will measure the sea quarks' Sivers Asymmetry, a left-right
asymmetry, with a target transversely polarized to the incoming 120 GeV proton beam. An online monitoring system has been developed to scan the polarized target system and polarization data while
integrating information from detectors and event reconstruction for near-continuous quality checking of the incoming data. Online monitoring of
the target system and detector package will play a vital role in ensuring
optimal performance of the target while achieving the highest figure of merit possible given the experimental circumstances. This novel monitoring system enhances the debugging process during commissioning and data acquisition through the use of machine learning pattern recognition techniques and anomaly detection. The scheme outlined promises to aid target operations by ensuring data quality and getting issues addressed soon via a system of alarms during the two-year-long production runs to begin 2024 at Fermilab.
DOI: https://doi.org/10.22323/1.456.0142
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