Designing a portable architecture for intelligent particle accelerator control

Klein, William B.; Stern, Carl R.; Luger, George F.; Olsson, E.

doi:10.1109/pac.1997.751227

Cited by 6 publications

(5 citation statements)

References 3 publications

(4 reference statements)

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“…distinguishing between bending magnet misalignment and field defects [44]). General AI/ML platform for beamline tuning were also planned [45][46][47][48]. None of these systems were eventually used routinely as part of an accelerator's main control system.…”

Section: Early History Of Usage For Particle Acceleratorsmentioning

confidence: 99%

Opportunities in Machine Learning for Particle Accelerators

Edelen,

Mayes,

Bowring

et al. 2018

Preprint

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Machine learning" (ML) is a subfield of artificial intelligence. The term applies broadly to a collection of computational algorithms and techniques that train systems from raw data rather than a priori models. ML techniques are now technologically mature enough to be applied to particle accelerators, and we expect that ML will become an increasingly valuable tool to meet new demands for beam energy, brightness, and stability. The intent of this white paper is to provide a high-level introduction to problems in accelerator science and operation where incorporating ML-based approaches may provide significant benefit. We review ML techniques currently being investigated at particle accelerator facilities, and we place specific emphasis on active research efforts and promising exploratory results. We also identify new applications and discuss their feasibility, along with the required data and infrastructure strategies. We conclude with a set of guidelines and recommendations for laboratory managers and administrators, emphasizing the logistical and technological requirements for successfully adopting this technology. This white paper also serves as a summary of the discussion from a recent workshop held at SLAC on ML for particle accelerators [1].

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Section: Early History Of Usage For Particle Acceleratorsmentioning

confidence: 99%

Opportunities in Machine Learning for Particle Accelerators

Edelen,

Mayes,

Bowring

et al. 2018

Preprint

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“…As part of one notable dedicated effort in the mid-1990s, Vista Control Systems and University of New Mexico collaborated on the development of an AI-based beamline tuning prototype [59,60,61,62]. Several studies also demonstrated the implementation of a distributed AI system for fault detection and management [63,64].…”

Section: ) Previous Efforts To Apply Neural Network To Particle Accmentioning

confidence: 99%

Neural Networks for Modeling and Control of Particle Accelerators

Edelen

Biedron

Chase

et al. 2016

IEEE Trans. Nucl. Sci.

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Abstract-Particle accelerators are host to myriad nonlinear and complex physical phenomena. They often involve a multitude of interacting systems, are subject to tight performance demands, and should be able to run for extended periods of time with minimal interruptions. Often times, traditional control techniques cannot fully meet these requirements. One promising avenue is to introduce machine learning and sophisticated control techniques inspired by artificial intelligence, particularly in light of recent theoretical and practical advances in these fields. Within machine learning and artificial intelligence, neural networks are particularly well-suited to modeling, control, and diagnostic analysis of complex, nonlinear, and time-varying systems, as well as systems with large parameter spaces. Consequently, the use of neural network-based modeling and control techniques could be of significant benefit to particle accelerators. For the same reasons, particle accelerators are also ideal test-beds for these techniques. Many early attempts to apply neural networks to particle accelerators yielded mixed results, due to the relative immaturity of the technology for such tasks. The purpose of this paper is to re-introduce neural networks to the particle accelerator community and report on some work in neural network control that is being conducted as part of a dedicated collaboration between Fermilab and Colorado State University (CSU). We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.

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“…The final beam should reach the target with a specific set of characteristics, as determined by the work being done. Figure 9 shows a simple accelerator beamline which includes trim magnets for steering, quad-Luger/Lewis/Stern rupole magnets for focusing, Faraday cups and stripline detectors for measuring current, and profile and popup monitors for measuring the size and position of the beam [Klein, 1997;Klein et al, 1997a].…”

Section: Representing Complex Knowledge and Skill: A Control System Fmentioning

confidence: 99%

“…We were able to take a software product that required several person months to build for use at one accelerator facility (the Brookhaven AFT) and to rebuild it in less than a person-week for use on another beamline (the Argonne ATLAS facility). All that was required was to reconfigure the existing objects to the particular situation at Argonne, and to add several new objects that were particular to that new facility [Klein et al, 1997a[Klein et al, , b, 2000.…”

Section: Representing Complex Knowledge and Skill: A Control System Fmentioning

confidence: 99%

Problem Solving as Model Refinement: Towards a Constructivist Epistemology

Luger

Lewis²,

Stern³

2002

Brain Behav Evol

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In recent years the Artificial Intelligence research group at the University of New Mexico have considered several areas of problem solving in interesting and complex domains. These areas have ranged from the low level explorations of a robot tasked to explore, map, and use a new environment to the development of very sophisticated control algorithms for the optimal use of particle beam accelerators. Although the results of our research have been reflected in computer-based problem solvers, such as the robot discovering and mapping out its world, these computational tasks are in many ways similar to expert human performance in similar tasks. In fact, in many important ways our computer-based approach mimics human expert performance in such domains. This paper describes three of these task domains as well as the software algorithms that have achieved competent performances therein. We conclude this paper with some comments on how software models of a domain can elucidate aspects of intellectual performance within that context. Furthermore, we demonstrate how exploratory problem solving along with model refinement algorithms can support a constructivist epistemology.

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Designing a portable architecture for intelligent particle accelerator control

Cited by 6 publications

References 3 publications

Opportunities in Machine Learning for Particle Accelerators

Opportunities in Machine Learning for Particle Accelerators

Neural Networks for Modeling and Control of Particle Accelerators

Problem Solving as Model Refinement: Towards a Constructivist Epistemology

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