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

被引：0

作者：

Jeske, Torri ^{[1
]}

McSpadden, Diana ^{[1
]}

Kalra, Nikhil ^{[1
]}

Britton, Thomas ^{[1
]}

Jarvis, Naomi ^{[2
]}

Lawrence, David ^{[1
]}

机构：

[1] Thomas Jefferson Natl Accelerator Facil, Newport News, VA 23518 USA

[2] Carnegie Mellon Univ, Dept Phys, Pittsburgh, PA 15213 USA

来源：

20TH INTERNATIONAL WORKSHOP ON ADVANCED COMPUTING AND ANALYSIS TECHNIQUES IN PHYSICS RESEARCH | 2023年 / 2438卷

基金：

美国能源部;

关键词：

D O I：

10.1088/1742-6596/2438/1/012132

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页数：6

共 2 条

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