Neural Networks for Modeling and Control of Particle Accelerators

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.Comment: 21 p

The EPICS Software Framework Moves from Controls to Physics

The Experimental Physics and Industrial Control System (EPICS), is an open-source software framework for high-performance distributed control, and is at the heart of many of the world’s large accelerators and telescopes. Recently, EPICS has undergone a major revision, with the aim of better computing supporting for the next generation of machines and analytical tools. Many new data types, such as matrices, tables, images, and statistical descriptions, plus users’ own data types, now supplement the simple scalar and waveform types of the former EPICS. New computational architectures for scientific computing have been added for high-performance data processing services and pipelining. Python and Java bindings have enabled powerful new user interfaces. The result has been that controls are now being integrated with modelling and simulation, machine learning, enterprise databases, and experiment DAQs. We introduce this new EPICS (version 7) from the perspective of accelerator physics and review early adoption cases in accelerators around the world

Proton beam steering control system for high precision radiotherapy at iThemba LABS : an investigation on actuator saturation constraints

Includes abstract.Includes bibliographical references (leaves 101-106).This thesis aims at studying some of the techniques used to deal with constraints with special application to the Proton beam steering control at iThemba LABS. The steering of charged particles occurring in research plants is one of the interests of control systems. In this work an investigation of the algorithm for the control of the proton beam steering system in the radiotherapy treatment facility at iThemba LABS is conducted. This algorithm is intended to autonomously maintain the beam centered with reference to the axis of the beamline, and keep the beam front parallel to the central axis of the beamline as stated by van Tubbergh and De Kock, 2006. Furthermore, the algorithm is responsible for monitoring the distribution of the proton beam, in a plane normal to the beam travel direction

CERN openlab Whitepaper on Future IT Challenges in Scientific Research

This whitepaper describes the major IT challenges in scientific research at CERN and several other European and international research laboratories and projects. Each challenge is exemplified through a set of concrete use cases drawn from the requirements of large-scale scientific programs. The paper is based on contributions from many researchers and IT experts of the participating laboratories and also input from the existing CERN openlab industrial sponsors. The views expressed in this document are those of the individual contributors and do not necessarily reflect the view of their organisations and/or affiliates

ARTIFICIAL INTELLIGENCE RESEARCH IN PARTICLE ACCELERATOR CONTROL SYSTEMS FOR BEAM LINE TUNING*

Abstract Tuning particle accelerators is time consuming and expensive, with a number of inherently non-linear interactions between system components. Conventional control methods have not been successful in this domain, and the result is constant and expensive monitoring of the systems by human operators. This is particularly true for the start-up and conditioning phase after a maintenance period or an unexpected fault. In turn, this often requires a step by step restart of the accelerator. Surprisingly few attempts have been made to apply intelligent accelerator control techniques to help with beam tuning, fault detection, and fault recovery problems. The reason for that might be that accelerator facilities are rare and difficult to understand systems that require detailed expert knowledge about the underlying physics as well as months if not years of experience to understand the relationship between individual components, particularly if they are geographically disjoint. This paper will give an overview about the research effort in the accelerator community that has been dedicated to the use of artificial intelligence methods for accelerator beam line tuning

Ultra-fast detector for wide range spectral measurements

KALYPSO is a novel detector operating at line rates above 10 Mfps. The detector board holds a silicon or InGaAs linear array sensor with spectral sensitivity ranging from 400 nm to 2600 nm. The sensor is connected to a cutting-edge, custom designed, ASIC readout chip which is responsible for the remarkable frame rate. The FPGA readout architecture enables continuous data acquisition and processing in real time. This detector is currently employed in many synchrotron facilities for beam diagnostics and for the characterization of self-built Ytterbium-doped fiber laser emitting around 1050 nm with a bandwidth of 40 nm