AI for Experimental Controls at Jefferson Lab

TL;DR

This paper presents an AI system designed to automate and improve the calibration process of detector systems at Jefferson Lab, enabling near real-time adjustments and reducing manual effort.

Contribution

It introduces a neural network-based approach for calibrating the Central Drift Chamber, shifting calibration from offline to near real-time at Jefferson Lab.

Findings

AI reduces calibration time significantly.

Neural network accurately predicts calibration constants.

System maintains detector performance during experiments.

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.