  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.
