Using AI to predict calibration constants for the central drift chamber in GlueX at Jefferson Lab

Conference · 31 December 2022 · Journal of Physics: Conference Series

DOI:https://doi.org/10.1088/1742-6596/2438/1/012132· OSTI ID:1959044

Jeske, Torri; McSpadden, Diana; Kalra, Nikhil; Britton, Thomas; Jarvis, Naomi; Lawrence, David

The AI for Experimental Controls project team at Jefferson Lab has developed an AI system to control and calibrate a large drift chamber system in near-real time. The AI system will monitor environmental and experimental variables to recommend voltage settings that maintain consistent dE/dx gain and optimal resolution throughout the experiment. At present, calibrations are performed after data have been recorded and require a considerable amount of time and attention from experts. The calibrations currently require multiple iterations and depend on accurate tracking information. Our approach uses environmental data, such as atmospheric pressure and gas temperature, and beam conditions, such as the flux of incident particles, as inputs to a Gaussian Process Regression (GPR) model. For the data taken during the GlueX 2020 run period, the GPR is able to predict the existing gain correction factors to within 3.5%. This talk will briefly describe the development, testing, and future plans for this system at Jefferson Lab.

• Research Organization: Thomas Jefferson National Accelerator Facility, Newport News, VA (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Nuclear Physics (NP)
• DOE Contract Number: AC05-06OR23177
• OSTI ID: 1959044
• Report Number(s): JLAB-CST-22-3573; DOE/OR/23177-5444
• Journal Information: Journal of Physics: Conference Series, Journal Name: Journal of Physics: Conference Series Journal Issue: 1 Vol. 2438; ISSN 1742-6588
• Country of Publication: United States
• Language: English