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Publication Information

Title AI for Experimental Controls at Jefferson Lab
Authors Torri Jeske, Naomi Jarvis, Helen McSpadden, Nikhil Kalra, David Lawrence, Thomas Britton
JLAB number JLAB-CST-22-3559
LANL number (None)
Other number DOE/OR/23177-5436
Document Type(s) (Meeting) 
Associated with EIC: No
Supported by Jefferson Lab LDRD Funding: No
Funding Source: Nuclear Physics (NP)

Invited Talk Paper compiled for AI4EIC 2021

Compiled for Journal of Instrumentation
Volume 17
Page(s) C03043
Publication Abstract: The AI for Experimental Controls project is developing an AI system to control and calibrate detector systems located at Jefferson Laboratory. Currently, calibrations are performed offline and require significant time and attention from experts. This work would reduce the amount of data and the amount of time spent calibrating in an offline setting. The first use case involves the Central Drift Chamber (CDC) located inside the GlueX spectrometer in Hall D. We use a combination of environmental and experimental data, such as atmospheric pressure, gas temperature, and the flux of incident particles as inputs to a Sequential Neural Network (NN) to recommend a high voltage setting and the corresponding calibration constants in order to maintain consistent gain and optimal resolution throughout the experiment. Utilizing AI in this manner represents an initial shift from offline calibration towards near real time calibrations performed at Jefferson Laboratory.
Experiment Numbers: other
Group: Scientific Computing
Document: pdf
Accepted Manuscript: AI4EIC_proceedings-51.pdf
Supporting Documents:
Supporting Datasets: