A Simulation Study of the Global Orbit Feedback System for Pohang Light Source

Kim, Kukhee; Shim, Kyuyeol; Choi, Jinhyuk; Cho, Moo‐Hyun; Namkung, W.; Ko, In Soo

doi:10.1143/jjap.39.6094

Cited by 6 publications

(3 citation statements)

References 2 publications

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“…In the early 1990s, scientists at Los Alamos National Lab had some experimental success with neural-network-based ion source control [34][35][36]. Other early studies at the University of New Mexico focused on orbit/trajectory control [37][38][39][40][41], fault detection and management [42,43], and root-cause analysis of errors (e.g. distinguishing between bending magnet misalignment and field defects [44]).…”

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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“…During the 1990s to mid-2000s, NNs in particular were investigated for orbit/trajectory control [68,69,70,71,72], with mixed results. Also during this time, a NN was successfully implemented to detect faulty beamline and diagnostic components [73].…”

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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“…In order to quantify the result of the orbit correction, we define the rms reduction rate as the ratio of the rms orbit distortion before and after the correction [4]. In this definition, we exclude the orbit distortion in the constraint region because our aim is to preserve the orbit in the constraint region.…”

Section: Choice Of Bpms and Correctorsmentioning

confidence: 99%

A Method of Closed Orbit Correction under Constraint Conditions

Kim¹,

Choi

Lee

et al. 2001

Jpn. J. Appl. Phys.

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We have developed the correction algorithm of the closed orbit distortion (COD) under the constraint condition where beam positions at particular points are not changed. The new algorithm is based on the modified PSI-NOM algorithm including the regularization process in order to avoid the inversion problem of the ill-posed response matrices. We have confirmed that the COD is well corrected with no orbit leak in the simulation and is in good agreement with the experiment.

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A Simulation Study of the Global Orbit Feedback System for Pohang Light Source

Cited by 6 publications

References 2 publications

Opportunities in Machine Learning for Particle Accelerators

Opportunities in Machine Learning for Particle Accelerators

Neural Networks for Modeling and Control of Particle Accelerators

A Method of Closed Orbit Correction under Constraint Conditions

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