Publications
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) |
|
Meeting Invited Talk Paper compiled for AI4EIC 2021 Journal Compiled for Journal of Instrumentation Volume 17 Page(s) C03043 Refereed | |
| 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: | |
| DOI: | doi.org/10.1088/1748-0221/17/03/c03043 |
| Accepted Manuscript: | AI4EIC_proceedings-51.pdf |
| Supporting Documents: | |
| Supporting Datasets: | |