STI Publications - View Publication Form #16923

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Publication Information
Title AI for Experimental Controls at Jefferson Lab
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.
Author(s) Torri Jeske, Naomi Jarvis, Diana McSpadden, Nikhil Kalra, David Lawrence, Thomas Britton
Publication Date March 2022
Document Type Meeting, Journal Article
Primary Institution Thomas Jefferson National Accelerator Facility, Newport News
Affiliation Comp Sci&Tech (CST) Div / Scientific Computing / Scientific Computing
Funding Source Nuclear Physics (NP)
Proprietary? No
This publication conveys Technical Science Results
Document Numbers
JLAB Number: JLAB-CST-22-3559 OSTI Number: 1872192
LANL Number: Other Number: DOE/OR/23177-5436
Associated with an experiment No
Associated with EIC No
Supported by Jefferson Lab LDRD Funding No
Meeting / Conference
Meeting Name AI4EIC 2021
Meeting Date 9/7/2021
Document Subtype Invited Talk (proceedings)
Journal Article
Journal Name Journal of Instrumentation
Refereed Yes
Volume 17
Issue
Page(s) C03043
Attachments/Datasets/DOI Link
Document(s)
AI4EIC_proceedings-51.pdf (STI Document)
AI4EIC_proceedings-51.pdf (Accepted Manuscript)
DOI Link
Dataset(s) (none)
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