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AI Driven Experiment Calibration and Control

Conference · · EPJ Web of Conferences
 [1];  [2];  [2];  [2];  [3];  [1];  [1];  [1];  [1]
  1. Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)
  2. Univ. of Virginia, Charlottesville, VA (United States)
  3. Carnegie Mellon Univ., Pittsburgh, PA (United States)
+ Show Author Affiliations

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 ±15% with fixed HV. We also demonstrate an implementation of an uncertainty aware system which exploits a key feature of a Gaussian process.

Research Organization:
Thomas Jefferson National Accelerator Facility, Newport News, VA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Nuclear Physics (NP)
DOE Contract Number:
AC05-06OR23177
OSTI ID:
2406483
Report Number(s):
JLAB-CST-23-3930; DOE/OR/23177-7156
Journal Information:
EPJ Web of Conferences, Journal Name: EPJ Web of Conferences Vol. 295; ISSN 2100-014X
Country of Publication:
United States
Language:
English

References (5)

The GlueX central drift chamber: Design and performance
  • Van Haarlem, Y.; Meyer, C. A.; Barbosa, F.
  • Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 622, Issue 1 https://doi.org/10.1016/j.nima.2010.06.272
journal October 2010
Gaussian Processes for Machine Learning book January 2005
Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial journal December 2023
Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position journal April 1980
The GlueX beamline and detector
  • Adhikari, S.; Akondi, C. S.; Al Ghoul, H.
  • Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 987 https://doi.org/10.1016/j.nima.2020.164807
journal January 2021

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