NAPAC2019 Tutorials
Tuesday, 3 September
Title: Beam Instrumentation and Measurement Challenges
Speaker: Michiko Minty, Accelerator Division, Brookhaven National Laboratory
Abstract: Since the inception of particle accelerators about 100 years ago, nearly every field of modern society has benefited from their continued development. Applications include discovery science (identification of basic matter constituents and their interactions), nuclear weapon and energy development, material and biological sciences as well as medical sciences including cancer therapies. Common to all accelerators is the need for accurate and precise measurements of the beam’s properties to optimize accelerator performance. This tutorial will cover select beam instrumentation basics, applications of these for characterizing accelerator performance, then present instrumentation challenges for future particle accelerators.
Bio: Michiko Minty is currently the Associate Division Head of the Accelerator Division at Brookhaven Lab and has led the C-AD's Instrumentation Systems group at Brookhaven Lab since arriving in March 2008. She earned a PhD in physics from Indiana University in 1991. She was a postdoc at Indiana University and the University of Michigan in 1991 and, at SLAC National Accelerator Laboratory, was a postdoc from 1991 to 1994 and a staff scientist from 1994 to 2000. She was a visiting scientist at the High Energy Accelerator Research Organization—KEK—in Japan in 1995 and at the Deutsches Electronen-Synchrotron (DESY) in Germany in 1999. From 2000 to 2005 she worked at DESY as head of the DESY-2 and DESY-3 lepton and hadron synchrotrons.
Wednesday, 4 September
Title: Adaptive machine learning and automatic tuning of intense electron bunches in particle accelerators
Speaker: Alexander Scheinker, Los Alamos National Laboratory
Abstract: Machine learning and in particular neural networks, have been around for a very long time. In recent years, thanks to growth in computing power, neural networks have reshaped many fields of research, including self driving cars, computers playing complex video games, image identification, and even particle accelerators. In this tutorial, I will first present an introduction to machine learning for beginners and will also touch on a few aspects of adaptive control theory. I will then introduce some problems in particle accelerators and present how they have been approached utilizing machine learning techniques as well as adaptive machine learning approaches, for automatically tuning extremely short and high intensity electron bunches in free electron lasers.
Bio: Alexander Scheinker received BA degrees in Math and Physics from Washington University in Saint Louis in 2006, a MA in math in 2008 and a PhD in nonlinear dynamics and control theory in 2012, both at the University of California, San Diego. Alex also received a MS in beam physics from Indiana University in 2013. During his PhD, Alex developed and analytically proved stability and convergence properties of an extremum seeking (ES) automated tuning algorithm which is applicable for unknown, many parameter, time-varying nonlinear systems for real time feedback control and optimization. Alex joined the RF Control Group at the Los Alamos National Laboratory in 2011 where he continued theoretical control theory research and also began to implement adaptive feedback algorithms for various accelerator subsystems. Alex has applied his algorithms in hardware at various particle accelerators around the world, including the SPEAR3 synchrotron light source for beam dynamics optimization via automatic magnet tuning, at the FACET wakefield accelerator for non-invasive longitudinal phase space diagnostics, at the LANSCE linear accelerator for automated RF buncher cavity phase tuning and in FPGA-based digital feedback controls for beam loading compensation. Recently, Alex began working on adaptive machine learning techniques, combining neural networks and model-independent feedback algorithms, which were applied on the LCLS and EuXFEL free electron lasers for automatic control of the longitudinal phase space of the electron beam and for light pulse energy maximization.
Thursday, 5 September
Title: Qubits, Beams and Fusion
Speaker: Thomas Schenkel, Accelerator Technology and Applied Physics Division (ATAP), Lawrence Berkeley National Laboratory
Abstract: Quantum information science (QIS) is bound to impact many areas of science and technology and the development of quantum information science capabilities is enabled by input from many areas. In this tutorial I will outline connections between QIS, particle accelerators and directions to advance our understanding of nuclear fusion with examples from our work at Berkeley Lab [1-4].
References
[1] A. Bienfait, et al., “Controlling spin relaxation with a cavity“, Nature 531, 74 (2016)
[2] C. P. Berlinguette, et al., “Revisiting the cold case of cold fusion“, Nature 570, 45 (2019)
[3] T. Schenkel, et al., https://arxiv.org/abs/1905.03400
[4] http://atap.lbl.gov/
Acknowledgments
This work was supported by the Director, Office of Science, Offices of High Energy Physics and Fusion Energy Sciences, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231, by LDRD funding at Berkeley Lab, and by Google LLC under CRADA between LBNL and Google LLC.
Bio: Thomas Schenkel is a physicist and senior scientist at Lawrence Berkeley National Laboratory, where he serves as the interim Director of the Accelerator Technology and Applied Physics Division. He obtained his PhD in physics from Goethe University, Frankfurt/M, for studies with beams of slow, highly charged ions. Following postdoctoral research at Lawrence Livermore National Laboratory, he joined Berkeley Lab in 2000 to work on the Spallation Neutron Source front end project. At Berkeley Lab, Thomas has established a program exploring spin qubits with deterministic ion placement, studies of spin dependent transport and recently on materials far from equilibrium. Thomas is teaching a graduate course on Beam Physics and Particle Accelerators in the Nuclear Engineering Department at UC Berkeley and has supervised a series of graduate students. His current research interests include advanced accelerator concepts, materials far from equilibrium, and the development of qubits for quantum sensing and communication.
friday, 6 September
Title: Rare Isotope Beams and High-power Accelerators
Speaker: Professor Jie Wei, Accelerator Systems Division, Facility for Rare Isotope Beams and Michigan State University
Abstract: Facilities for rare isotope beams provide tools for nuclear physics research and applications ranging from fundamental nuclear structure and dynamics to societal benefits in medicine, energy, material sciences and national security. State-of-the-art rare isotope facilities can be based on isotope separation on-line (ISOL) approach using mostly high-power proton beams striking upon a thick target, or fragment separation approach using high-power heavy ion beams striking upon a thin target followed by fragment separation. This tutorial class introduces high power hadron accelerators as driver machines for rare isotope production, summarizing the key design philosophy, physical and technical challenges, and current world-wide development status. As an example, the Facility for Rare Isotope Beams (FRIB) project is used to illustrate the process of establishing such facilities.
Bio: Jie Wei is the accelerator systems division director of the Facility for Rare Isotope Beams (FRIB) project and professor at Michigan State University. He received his PhD in physics in 1989 from State University of New York at Stony Brook. He is presently responsible for the design, construction, commissioning and operations of the FRIB accelerator systems.
