Publications
Publication Information
| Title | AI Driven Experiment Calibration and Control |
| Authors | Thomas Britton, Cullan Bedwell, Abhijeet Chawhan, Julie Crow, Naomi Jarvis, Torri Jeske, Nikhil Kalra, David Lawrence, Helen McSpadden |
| JLAB number | JLAB-CST-23-3930 |
| LANL number | (None) |
| Other number | DOE/OR/23177-7156 |
| Document Type(s) | (Meeting) |
| Associated with EIC: | No |
| Supported by Jefferson Lab LDRD Funding: | No |
| Funding Source: | Nuclear Physics (NP) |
|
Meeting Paper compiled for CHEP 2023 Proceedings CHEP 2023 Edited By EPJ Web of Conferences (2023) | |
| Publication Abstract: | One critical step on the path from data taking to physics analysis is calibration. For many experiments this step is both time consuming and computationally expensive. The AI Experimental Calibration and Control project seeks to address these issues, starting first with the GlueX Central Drift Chamber (CDC). We demonstrate the ability of a Gaussian Process to estimate the gain correction factor (GCF) of the GlueX CDC accurately, and also the uncertainty of this estimate. Using the estimated GCF, the developed system infers a new high voltage (HV) setting that stabilizes the GCF in the face of changing environmental conditions. This happens in near real time during data taking and produces data which are already approximately gain-calibrated, eliminating the cost of performing those calibrations which vary $\pm$ 15\% with fixed HV. We also demonstrate an implementation of an uncertainty aware system which exploits a key feature of a Gaussian process. |
| Experiment Numbers: | other |
| Group: | Scientific Computing |
| Document: | |
| DOI: | https://doi.org/10.1051/epjconf/202429502003 |
| Accepted Manuscript: | epjconf_chep2024_02003.pdf |
| Supporting Documents: | |
| Supporting Datasets: | |