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

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