@article{Jeske_2022,
	doi = {10.1088/1748-0221/17/03/c03043},
	url = {https://doi.org/10.1088/1748-0221/17/03/c03043},
	year = 2022,
	month = {mar},
	publisher = {{IOP} Publishing},
	volume = {17},
	number = {03},
	pages = {C03043},
	author = {T. Jeske and D. McSpadden and N. Kalra and T. Britton and N. Jarvis and D. Lawrence},
	title = {{AI} for Experimental Controls at Jefferson Lab},
	journal = {Journal of Instrumentation},
	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.}
}