Run-time environment and application tools for the ground test accelerator control system

Kozubal, A.J.; Kerstiens, D.; Hill, J.O.; Dalesio, Leo

doi:10.1016/0168-9002(90)91446-i

Cited by 10 publications

(4 citation statements)

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“…The atmospheric pressure, gas temperature, and the current drawn by the CDC HV boards are measured throughout the duration of an experiment and are readily accessible via the Experimental Physics Industrial Controls System (EPICS) archive [3]. For training, 601 production quality runs from the 2020 and 2021 run periods were used.…”

Section: Data and Model Selectionmentioning

confidence: 99%

AI Driven Experiment Calibration and Control

Britton,

Bedwell,

Chawhan

et al. 2024

EPJ Web of Conf.

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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.

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Section: Data and Model Selectionmentioning

confidence: 99%

AI Driven Experiment Calibration and Control

Britton,

Bedwell,

Chawhan

et al. 2024

EPJ Web of Conf.

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“…The EPICS on VULCAN is used as the overarching system for motor control with the neutron event distributor (nED) data acquisition [21]. These systems work in tandem to facilitate efficient experimental control for neutron diffraction experiments.…”

Section: Real-time Reduction Streaming Peak Fitting and Strain Comput...mentioning

confidence: 99%

Adaptive sampling for accelerating neutron diffraction-based strain mapping ^*

Venkatakrishnan

Fancher

Ziatdinov³

et al. 2023

Mach. Learn.: Sci. Technol.

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Neutron diffraction is a useful technique for mapping residual strains in dense metal objects. The technique works by placing an object in the path of a neutron beam, measuring the diffracted signals and inferring the local lattice strain values from the measurement. In order to map the strains across the entire object, the object is stepped one position at a time in the path of the neutron beam, typically in raster order, and at each position a strain value is estimated. Typical dwell times at neutron diffraction instruments result in an overall measurement that can take several hours to map an object that is several tens of centimeters in each dimension at a resolution of a few millimeters, during which the end users do not have an estimate of the global strain features and are at risk of incomplete information in case of instruments outages. In this paper, we propose an object adaptive sampling strategy to measure the significant points first. We start with a small initial uniform set of measurement points across the object to be mapped, compute the strain in those positions and use a machine learning technique to predict the next position to measure in the object. Specifically,we use a Bayesian optimization based on a Gaussian process regression method to infer the underlying strain field from a sparse set of measurements and predict the next most informative positions to measure based on estimates of the mean and variance in the strain fields estimated from the previously measured points. We demonstrate our real-time measure-infer-predict workflow on additively manufactured steel parts- demonstrating that we can get an accurate strain estimate even with 30-40% of the typical number of measurements - leading the path to faster strain mapping with useful real-time feedback. We emphasize that the proposed method is general and can be used for fast mapping of other material properties such as phase fractions from time-consuming point-wise neutron measurements.

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“…The controlsystem for AFEL [9] is developed using the Experimental Physics and IndustrialControl System (EPICS) [10] developed at the Los Alamos National Laboratory. The EPICS provides distributed processing within a high performance run time environment, and includes a comprehensive set of applications development tools.…”

Section: Computer-basedcontrol Systemmentioning

confidence: 99%