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.

/pdf/arxiv-opportunities-in-machine-learning-for-particle-zzvjvx0u3a.pdf

arXiv : Opportunities in Machine Learning for Particle Accelerators

Plasma density of an ion source is an important parameter to be measured for the characterization of an ion source. Measurement of plasma density depends on several input parameters such as input power, gas pressure, magnetic, plasma potentials, and electron temperature. In the absence of a direct mathematical relationship between the input parameters and plasma density, measuring plasma density becomes a time-consuming task and has been done through invasive experimental methods. This paper utilizes decision tree and random forest algorithms for developing a predictive model of a negative hydrogen helicon plasma source to estimate the plasma density from the RF input power, magnetic field (B), and gas pressure. For this purpose, a dataset having 162 samples have been applied for training, testing and validation of the proposed models. A comparative analysis of proposed models has been done through by comparing of root mean square error and the coefficient of determination (R2) between the measured and the predicted values of plasma density. The obtained results show that the random forests model has shown better results and able to predict plasma values closer to the actual experimental results. Also, random forests algorithm can interpret the complex non-linear relationships between the influential input parameters and the output.

Plasma Density Prediction for Helicon Negative Hydrogen Plasma Source Using Decision Tree and Random Forest Algorithm

Tuning and controlling particle accelerators is time consuming and expensive. Inherently nonlinear, the control problem is one to which conventional methods cannot satisfactorily be applied. Advanced information technologies such as expert systems and neural networks have been applied separately to the problem, with isolated success. Few, if any, of these advanced information technologies have been applied for general use or in a manner useful to multiple accelerator installations. We discuss results of coupling neural network and expert systems technology to solve several standard accelerator tuning problems based on realistic simulations. We also examine the effectiveness of additional heuristic search techniques such as genetic algorithms. Finally, we show the integration of this hybrid AI system with an existing general-purpose control system.

/pdf/framework-for-a-general-purpose-intelligent-control-system-4t9upxwnwh.pdf

Framework for a general purpose, intelligent control system for particle accelerators

As part of the upgrade of the Los Alamos Meson Physics Facility (LAMPF) control computer, the software providing the interface between application programs and the accelerator hardware was redesigned. This new Data System design - which handles more than 12,000 widely different devices - allows greater uniformity, flexibility, maintainability, and hardware independence than before. The Data System portion of the LAMPF Control System is described in this paper.

/pdf/data-acquisition-software-for-the-lampf-control-system-4jlw1rbr9z.pdf

Data Acquisition Software for the LAMPF Control System

An expert system type of knowledge rule base has been developed for the input parameters used by the particle beam transport program TRACE 3-D. The goal has been to provide the program's user with adequate on-screen information to allow him to initially set up a problem with minimal "off-line" calculations. The focus of this work has been in developing rules for the parameters which define the beam line transport elements. Ten global parameters, the particle mass and charge, beam energy, etc., are used to provide "expert" estimates of lower and upper limits for each of the transport element parameters. For example, the limits for the field strength of the quadrupole element are based on a water-cooled, iron-core electromagnet with dimensions derived from practical engineering constraints, and the upper limit for the effective length is scaled with the particle momenta so that initially parallel trajectories do not cross the axis inside the magnet. Limits for the quadrupole doubler and triplet parameters incorporate these rules and additional rules based on stable FODO lattices and bidirectional focusing requirements. The structure of the rule base is outlined and examples for the quadrupole singlet, doublet and triplet are described. The rule base has been implemented within the Shell for Particle Accelerator Related Codes (SPARC) graphical user interface (GUI). >

Knowledge rule base for the beam optics program TRACE 3-D

This paper discusses the present state of the LAMPF accelerator control computer upgrade. This includes a summary of our recent operational experience and the status of the application programs in the new system. In addition, we describe several optimization techniques which have been used to improve system-wide performance, and a limited VAX/VMS operating system environment which has been made available to the accelerator operators. We also describe our plans for upgrading the LAMPF control system computer network.

/pdf/the-status-of-the-lampf-control-system-upgrade-28w6i1m4ox.pdf

The status of the LAMPF control system upgrade

On-Line Replacement of a Particle Accelerator Control Computer.

The experience of developing an expert system to aid in the tuning of the Ion Source Injection beam line at TRIUMF is described. The challenging and complex task of introducing expert-system technology into an established accelerator operation is outlined. Success in this environment depends strongly on the choice of project, the choice of experts, the choice of tools, and the methods used to represent the expertise. All these choices are discussed.

/pdf/the-development-of-an-expert-system-to-tune-a-beam-line-46u874ojm6.pdf

The development of an expert system to tune a beam line

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The status of the LAMPF control system upgrade

On-Line Replacement of a Particle Accelerator Control Computer.

The development of an expert system to tune a beam line