NAPAC2022 - Classification: 09: Computing and Data Science for Accelerator Systems

Paper	Title	Page
MOPA01	Realistic CAD-Based Geometries for Arbitrary Magnets with Beam Delivery Simulation (BDSIM)	55
	E. Ramoisiaux, R. Dantinne, E. Gnacadja, C. Hernalsteens, S. Musibau, B. Ndihokubwayo, N. Pauly, R. Tesse, M. Vanwelde ULB, Bruxelles, Belgium S.T. Boogert, L.J. Nevay, W. Shields Royal Holloway, University of London, Surrey, United Kingdom C. Hernalsteens CERN, Meyrin, Switzerland
	Monte Carlo simulations are required to evaluate beam losses and secondary radiation accurately in particle accelerators and beamlines. Detailed CAD geometries are critical to account for a realistic distribution of material masses but increase the model complexity and often lead to code duplication. Beam Delivery Simulation (BDSIM) and the Python package pyg4ometry enable handling such accelerator models within a single, simplified workflow to run complete simulations of primary and secondary particle tracking and interactions with matter using Geant4 routines. Additional capabilities have been developed to model arbitrary bent magnets by associating externally modeled geometries to the magnet poles, yoke, and beampipe. Individual field descriptions can be associated with the yoke and vacuum pipe separately to provide fine-grained control of the magnet model. The implementation of these new features is described in detail and applied to the modeling of the CERN Proton Synchrotron (PS) combined function magnets.
	Poster MOPA01 [0.781 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA01
About •	Received ※ 02 August 2022 — Revised ※ 07 August 2022 — Accepted ※ 09 August 2022 — Issue date ※ 16 September 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

MOPA08	Beamline Optimization Methods for High Intensity Muon Beams at PSI	63
	E.V. Valetov PSI, Villigen PSI, Switzerland
	Funding: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 884104 (PSI-FELLOW-III-3i). We perform beamline design optimization for the High Intensity Muon Beams (HIMB) project at the Paul Scherrer Institute (PSI), which will deliver muon beams at the unprecedented rate of 1·10¹⁰ muons/s to next-generation intensity frontier particle physics and material science experiments. For optimization of the design and operational parameters to maximize the beamline transmission, we use the asynchronous Bayesian optimization package DeepHyper and a custom build of G4beamline with variance reduction and measured cross sections. We minimize the beam spot size at the final foci using a COSY INFINITY model with differential-algebraic system knobs, where we minimize the respective transfer map elements using the Levenberg-Marquardt and simulated annealing optimizers. We obtained a transmission of 1.34·10¹⁰ muons/s in a G4beamline model of HIMB’s MUH2 beamline into the experimental area.
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA08
About •	Received ※ 02 August 2022 — Revised ※ 08 August 2022 — Accepted ※ 11 August 2022 — Issue date ※ 23 August 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

MOPA13	Design of a Surrogate Model for MUED at BNL Using VSim, Elegant and HPC	72
	S.I. Sosa Guitron, S. Biedron, T.B. Bolin UNM-ECE, Albuquerque, USA S. Biedron Element Aero, Chicago, USA S. Biedron UNM-ME, Albuquerque, New Mexico, USA
	Funding: U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Program of Electron and Scanning Probe Microscopes, award number DE-SC0021365. The MeV Ultrafast Electron Diffraction (MUED) instrument at Brookhaven National Laboratory is a unique capability for material science. As part of a plan to make MUED a high-throughput user facility, we are exploring instrumentation developments based on Machine Learning (ML). We are developing a surrogate model of MUED that can be used to support control tasks. The surrogate model will be based on beam simulations that are benchmarked to experimental observations. We use VSim to model the beam dynamics of the radio-frequency gun and Elegant to transport the beam through the rest of the beam-line. We also use High Performance Computing resources from Argonne Leadership Computing Facility to generate the data for the surrogate model based on the original simulation as well as training the ML model.
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA13
About •	Received ※ 01 August 2022 — Revised ※ 09 August 2022 — Accepted ※ 11 August 2022 — Issue date ※ 21 August 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

MOPA18	Residual Dose and Environmental Monitoring for the Fermilab Main Injector Tunnel Using the Data Acquisition Logging Engine (Dale)	87
	N. Chelidze, R. Ainsworth, B.C. Brown, D. Capista, K.J. Hazelwood, D.K. Morris, M.J. Murphy Fermilab, Batavia, Illinois, USA
	Funding: Fermi National Accelerator Laboratory The Recycler and the Main Injector are part of the Fermilab Accelerator complex used to deliver proton beam to the different experiments. It is very important to control and minimize losses in both machines during operation, to reduce personnel dose from residual activation and to preserve component lifetime. To minimize losses, we need to identify the loss points and adjust the components accordingly. The Data Acquisition Loss Engine (DALE) platform has been developed within the Main Injector department and upgraded throughout the years. DALE is used to survey the entire enclosure for residual dose rates and environmental readings when unrestricted access to the enclosure is possible. Currently DALE has two radiation meters, which are aligned along each machine, so loss points can be identified for both at the same time. DALE attaches to the enclosure carts and is continuously in motion monitoring dose rates and other environmental readings. In this paper we will describe how DALE is used to provide radiation maps of the residual dose rates in the enclosure. We will also compare the loss points with the Beam Loss monitor data.
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA18
About •	Received ※ 02 August 2022 — Revised ※ 05 August 2022 — Accepted ※ 11 August 2022 — Issue date ※ 21 September 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

MOPA44	Utilizing Python to Prepare the VENUS Ion Source for Machine Learning	151
	A. Kireeff, L. Phair, M.J. Regis, M. Salathe, D.S. Todd LBNL, Berkeley, California, USA
	Funding: This work was supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under Contract No. DE-AC02-05CH11231. The fully superconducting electron cyclotron resonance (ECR) ion source VENUS is one of the world’s two highest-performing ECR ion sources, and a copy of this source will soon be used to produce ion beams at FRIB. The tuning and optimization of ECR ion sources is time consuming, and there are few detailed theoretical models to guide this work. To aid in this process, we are working toward utilizing machine learning to both efficiently optimize VENUS and reliably maintain its stability for long campaigns. We have created a Python library to interface with the programmable logic controller (PLC) in order to operate VENUS and collect and store source and beam data. We will discuss the design and safety considerations that went into creating this library, the implementation of the library, and some of the capabilities it enables.
	Poster MOPA44 [0.862 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA44
About •	Received ※ 17 July 2022 — Revised ※ 27 July 2022 — Accepted ※ 05 August 2022 — Issue date ※ 16 August 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

MOPA55	Facilitating Machine Learning Collaborations Between Labs, Universities, and Industry	164
	J.P. Edelen, D.T. Abell, D.L. Bruhwiler, S.J. Coleman, N.M. Cook, A. Diaw, J.A. Einstein-Curtis, C.C. Hall, M.C. Kilpatrick, B. Nash, I.V. Pogorelov RadiaSoft LLC, Boulder, Colorado, USA K.A. Brown BNL, Upton, New York, USA S. Calder ORNL RAD, Oak Ridge, Tennessee, USA A.L. Edelen, B.D. O’Shea, R.J. Roussel SLAC, Menlo Park, California, USA C.M. Hoffmann ORNL, Oak Ridge, Tennessee, USA E.-C. Huang LANL, Los Alamos, New Mexico, USA P. Piot Northern Illinois University, DeKalb, Illinois, USA C. Tennant JLab, Newport News, Virginia, USA
	It is clear from numerous recent community reports, papers, and proposals that machine learning is of tremendous interest for particle accelerator applications. The quickly evolving landscape continues to grow in both the breadth and depth of applications including physics modeling, anomaly detection, controls, diagnostics, and analysis. Consequently, laboratories, universities, and companies across the globe have established dedicated machine learning (ML) and data-science efforts aiming to make use of these new state-of-the-art tools. The current funding environment in the U.S. is structured in a way that supports specific application spaces rather than larger collaboration on community software. Here, we discuss the existing collaboration bottlenecks and how a shift in the funding environment, and how we develop collaborative tools, can help fuel the next wave of ML advancements for particle accelerators.
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA55
About •	Received ※ 10 August 2022 — Revised ※ 11 August 2022 — Accepted ※ 22 August 2022 — Issue date ※ 01 September 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

MOPA57	Online Models for X-Ray Beamlines	170
	B. Nash, D.T. Abell, M.V. Keilman, P. Moeller, I.V. Pogorelov RadiaSoft LLC, Boulder, Colorado, USA Y. Du, A. Giles, J. Lynch, T. Morris, M.S. Rakitin, A. Walter BNL, Upton, New York, USA N.B. Goldring STATE33 Inc., Portland, Oregon, USA
	Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Science, under Award Number DE-SC0020593 X-ray beamlines transport synchrotron radiation from the magnetic source to the sample at a synchrotron light source. Alignment of elements such as mirrors and gratings are often done manually and can be quite time consuming. The use of photon beam models during operations is not common in the same way that they are used to great benefit for particle beams in accelerators. Linear and non-linear optics including the effects of coherence may be computed from source properties and augmented with measurements. In collaboration with NSLS-II, we are developing software tools and methods to include the model of the x-ray beam as it passes on its way to the sample. We are integrating the Blue-Sky beamline control toolkit with the Sirepo interface to several x-ray optics codes. Further, we are developing a simplified linear optics approach based on a Gauss-Schell model and linear canonical transforms as well as developing Machine Learning models for use directly from diagnostics data. We present progress on applying these ideas on NSLS-II beamlines and give a future outlook on this rather large and open domain for technological development.
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA57
About •	Received ※ 27 July 2022 — Revised ※ 02 August 2022 — Accepted ※ 07 August 2022 — Issue date ※ 11 August 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

MOPA89	RHIC Electron Beam Cooling Analysis Using Principle Component and Autoencoder Analysis	260
	A.D. Tran, Y. Hao FRIB, East Lansing, Michigan, USA X. Gu BNL, Upton, New York, USA
	Funding: Work supported by the US Department of Energy under contract No. DE-AC02-98CH10886. Principal component analysis and autoencoder analysis were used to analyze the experimental data of RHIC operation with low energy RHIC electron cooling (LEReC). This is unsupervised learning which includes electron beam settings and observable during operation. Both analyses were used to gauge the dimensional reducibility of the data and to understand which features are important to beam cooling.
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA89
About •	Received ※ 02 August 2022 — Revised ※ 05 August 2022 — Accepted ※ 06 August 2022 — Issue date ※ 12 August 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUXE1	The Importance of Data, High-Performance Computing, and Artificial Intelligence/Machine Learning
	C.M. Sweeney LANL, Los Alamos, New Mexico, USA A.L. Edelen SLAC, Menlo Park, California, USA D.E. Martin ANL, Lemont, Illinois, USA
	As existing accelerator facilities are upgraded and new facilities come online, data volumes and velocity are increasing even with shorter data collection times. High-performance computing (HPC) systems doing simulation, data analytics and artificial intelligence/machine learning (AI/ML) are playing a major role in pre-experiment planning, design of experiments, real-time beam line and experiment analysis and control, and post-run data processing. Simulation and AI incorporated into experimental data analysis workflows are making efficient use of expensive facilities and accelerating scientific discoveries. HPC is experiencing its own growth, with exascale computers and AI acceleration coming online at several supercomputer centers. AI/ML is in the midst of rapid growth of techniques and expansion into new application areas. This session will focus on current and emerging technologies in HPC, experimental workflows, and AI/ML techniques to help you incorporate them into your own research. Dr. D. Martin will provide "HPC Overview" followed by "Workflows" by Dr. C. Sweeney. "AI and ML" by Dr. A. Edelen will be followed by community discussions and questions from the audience.
	Slides TUXE1 [11.946 MB]
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUXE2	High Performance Computing - DOE Facilities, Direction and Applications
	D.E. Martin ANL, Lemont, Illinois, USA
	Funding: This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. High-performance computing (HPC) systems doing simulation, data analytics and machine learning are playing a major role in accelerator physics, including pre-experiment planning, design of experiments, real-time beam line analysis and control, and post-run data processing. DOE high performance computer user facilities are evolving with new architectures, faster systems and a focus on integrating with experimental facilities.
	Slides TUXE2 [16.438 MB]
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUXE3	Artificial Intelligence and Machine Learning for Particle Accelerators
	A.L. Edelen SLAC, Menlo Park, California, USA
	As existing accelerator facilities are upgraded and new facilities come online, data volumes and velocity are increasing even with shorter data collection times. High-performance computing (HPC) systems doing simulation, data analytics and artificial intelligence/machine learning (AI/ML) are playing a major role in pre-experiment planning, design of experiments, real-time beam line and experiment analysis and control, and post-run data processing. Simulation and AI incorporated into experimental data analysis workflows are making efficient use of expensive facilities and accelerating scientific discoveries. HPC is experiencing its own growth, with exascale computers and AI acceleration coming online at several supercomputer centers. AI/ML is in the midst of rapid growth of techniques and expansion into new application areas. This session will focus on current and emerging technologies in HPC, experimental workflows, and AI/ML techniques to help you incorporate them into your own research. Dr. D. Martin will provide "HPC Overview" followed by "Workflows" by Dr. C. Sweeney. "AI and ML" by Dr. A. Edelen will be followed by community discussions and questions from the audience.
	Slides TUXE3 [17.252 MB]
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUYE1	Coulomb Crystals in Storage Rings for Quantum Information Science	296
	K.A. Brown BNL, Upton, New York, USA A. Aslam, S. Biedron, T.B. Bolin, C. Gonzalez-Zacarias, S.I. Sosa Guitron UNM-ECE, Albuquerque, USA B. Huang SBU, Stony Brook, USA T.G. Robertazzi Stony Brook University, Stony Brook, New York, USA
	Quantum information science is a growing field that promises to take computing into a new age of higher performance and larger scale computing as well as being capable of solving problems classical computers are incapable of solving. The outstanding issue in practical quantum computing today is scaling up the system while maintaining interconnectivity of the qubits and low error rates in qubit operations to be able to implement error correction and fault-tolerant operations. Trapped ion qubits offer long coherence times that allow error correction. However, error correction algorithms require large numbers of qubits to work properly. We can potentially create many thousands (or more) of qubits with long coherence states in a storage ring. For example, a circular radio-frequency quadrupole, which acts as a large circular ion trap and could enable larger scale quantum computing. Such a Storage Ring Quantum Computer (SRQC) would be a scalable and fault tolerant quantum information system, composed of qubits with very long coherence lifetimes.
	Slides TUYE1 [8.834 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUYE1
About •	Received ※ 17 July 2022 — Revised ※ 02 August 2022 — Accepted ※ 08 August 2022 — Issue date ※ 11 August 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUYE2	Next Generation Computational Tools for the Modeling and Design of Particle Accelerators at Exascale	302
	A. Huebl, R. Lehé, C.E. Mitchell, J. Qiang, R.D. Ryne, R.T. Sandberg, J.-L. Vay LBNL, Berkeley, USA
	Funding: Work supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. DOE SC and the NNSA, resources of NERSC, and by LBNL LDRD under DOE Contract DE-AC02-05CH11231. Particle accelerators are among the largest, most complex devices. To meet the challenges of increasing energy, intensity, accuracy, compactness, complexity and efficiency, increasingly sophisticated computational tools are required for their design and optimization. It is key that contemporary software take advantage of the latest advances in computer hardware and scientific software engineering practices, delivering speed, reproducibility and feature composability for the aforementioned challenges. A new open source software stack is being developed at the heart of the Beam pLasma Accelerator Simulation Toolkit (BLAST) by LBNL and collaborators, providing new particle-in-cell modeling codes capable of exploiting the power of GPUs on Exascale supercomputers. Combined with advanced numerical techniques, such as mesh-refinement, and intrinsic support for machine learning, these codes are primed to provide ultrafast to ultraprecise modeling for future accelerator design and operations. [1] J.-L. Vay, A. Huebl, et al, Phys. Plasmas 28, 023105 (2021) [2] J.-L. Vay, A. Huebl, et al, J. Instr. 16, T10003 (2021) [3] A. Myers, et al (incl. A. Huebl), Parallel Comput. 108, 102833 (2021)
	Slides TUYE2 [9.399 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUYE2
About •	Received ※ 13 July 2022 — Revised ※ 02 August 2022 — Accepted ※ 08 August 2022 — Issue date ※ 11 August 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUYE3	An Open-Source Based Data Management and Processing Framework on a Central Server for Scientific Experimental Data	307
	A. Liu, J.R. Callahan, S. Poddar, W. Si Euclid TechLabs, Solon, Ohio, USA J. Gao AJS Smartech LLC, Naperville, TX, USA
	Funding: This work is supported by the US DOE SBIR program under contract number DE-SC0021512. The ever-expanding size of accelerator operation and experimental data including those generated by electron microscopes and beamline facilities renders most proprietary software inefficient at managing data. The Findability, Accessibility, Interoperability, and Reuse (FAIR) principles of digital assets require a convenient platform for users to share and manage data on. An open-source data framework for storing raw data and metadata, hosting databases, and providing a platform for data processing and visualization is highly desirable. In this paper, we present an open-source, infrastructure-independent data management software framework, named by Euclid-NexusLIMS, to archive, register, record, visualize and process experimental data. The software was targeted initially for electron microscopes, but can be widely applied to all scientific experimental data.
	Slides TUYE3 [5.891 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUYE3
About •	Received ※ 04 August 2022 — Revised ※ 07 August 2022 — Accepted ※ 08 August 2022 — Issue date ※ 24 August 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUYE4	Machine Learning for Anomaly Detection and Classification in Particle Accelerators	311
	I. Lobach, M. Borland, K.C. Harkay, N. Kuklev, A. Sannibale, Y. Sun ANL, Lemont, Illinois, USA
	Funding: The work is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357. We explore the possibility of using a Machine Learning (ML) algorithm to identify the source of occasional poor performance of the Particle Accumulator Ring (PAR) and the Linac-To-PAR (LTP) transport line, which are parts of the injector complex of the Advanced Photon Source (APS) at Argonne National Lab. The cause of reduced injection or extraction efficiencies may be as simple as one parameter being out of range. Still, it may take an expert considerable time to notice it, whereas a well-trained ML model can point at it instantly. In addition, a machine expert might not be immediately available when a problem occurs. Therefore, we began by focusing on such single-parameter anomalies. The training data were generated by creating controlled perturbations of several parameters of PAR and LTP one-by-one, while continuously logging all available process variables. Then, several ML classifiers were trained to recognize certain signatures in the logged data and link them to the sources of poor machine performance. Possible applications of autoencoders and variational autoencoders for unsupervised anomaly detection and for anomaly clustering were considered as well.
	Slides TUYE4 [9.534 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUYE4
About •	Received ※ 03 August 2022 — Revised ※ 07 August 2022 — Accepted ※ 08 August 2022 — Issue date ※ 28 August 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUYE5	Multiobjective Optimization of the LCLS-II Photoinjector
	N.R. Neveu SLAC, Menlo Park, California, USA T.H. Chang, S.T.P. Hudson, J.M. Larson ANL, Lemont, Illinois, USA P.L. Franz Stanford University, Stanford, California, USA
	Genetic algorithms and particle swarm optimization are currently the most widely used optimization algorithms in the accelerator physics community. These algorithms can take many evaluations to find optimal solutions for one machine prototype. In this work, the LCLS-II photoinjector is optimized with three optimization algorithms: a genetic algorithm, a surrogate based algorithm, and a multi-start scalarization method. All three algorithms were able to optimize the photoinjector, with various trade-offs for each method discussed here.
	Slides TUYE5 [3.919 MB]
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUYE6	High-Fidelity Simulations and Machine Learning for Accelerator Design and Optimization
	D. Winklehner MIT, Cambridge, Massachusetts, USA A. Adelmann PSI, Villigen PSI, Switzerland
	Funding: NSF #1505858, NSF #162606, NSF #1626069 Computation has become a critically important tool for particle accelerator design and optimization. Thanks to massively parallel codes running on high-performance clusters, we can now accurately predict emergent properties of particle ensembles and non-linear collective effects, and use machine learning (ML) for analysis and to create "virtual twins" of accelerator systems. Here, we will present the IsoDAR experiment in neutrino physics as an example. For it, we have developed a compact and cost-effective cyclotron-based driver to produce very high-intensity beams. The system will be able to deliver cw proton currents of 10 mA on target in the energy regime around 60 MeV. 10 mA is a factor of 10 higher than commercially available machines. This increase in current is possible due to longitudinal-radial coupling through space charge, an effect dubbed "vortex motion". We will discuss the high-fidelity OPAL simulations performed to simulate this effect in the IsoDAR cyclotron and predict beam losses due to halo formation. We will present uncertainty quantification for this design and we will show our study to optimize the IsoDAR injector RFQ using ML.
	Slides TUYE6 [2.414 MB]
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUZG1	Magnets to ML to Light Sources: Designing from the Browser with Sirepo
	J.P. Edelen, B. Nash RadiaSoft LLC, Boulder, Colorado, USA R.J. Roussel SLAC, Menlo Park, California, USA
	Join us for an afternoon intensive with the Sirepo CAE & Design platform! Experts will present their work and lead tutorials using Sirepo’s apps and tools for magnet design, ML techniques in control systems, and X-ray beamline modeling. Bring your own laptop and pre-register to use the free gateway at Sirepo.com for practical exercises. Dr. Edelen will provide a tutorial on how to design dipoles and undulators using parameterized magnets. Dr. Roussel will demonstrate combining the classical Preisach model of hysteresis with ML techniques to efficiently create non-parametric, high-fidelity models of arbitrary systems exhibiting hysteresis. Also shown will be how using these joint hysteresis-beam models allows users to overcome optimization performance limitations when hysteresis effects are ignored. Dr. Nash will review the capabilities of Shadow and SRW within Sirepo, discuss additional optics tools in the interface including brightness and linear optics, demonstrate translation between the two codes, and show how to continue your simulation work in a python based Jupyter notebook.
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPA14	Fast First-Order Spin Propagation for Spin Matching and Polarization Optimization with Bmad	369
	J.M. Asimow, G.H. Hoffstaetter, D. Sagan, M.G. Signorelli Cornell University (CLASSE), Cornell Laboratory for Accelerator-Based Sciences and Education, Ithaca, New York, USA
	Accurate spin tracking is essential for the simulation and propagation of polarized beams, in which a majority of the particles’ spin point in the same direction. Bmad, an open-sourced library for the simulation of charged particle dynamics, traditionally tracks spin via integrating through each element of a lattice. While exceptionally accurate, this method has the drawback of being slow; at best, the runtime is proportional to the length of the element. By solving the spin transport equation for simple magnet elements, Bmad can reduce this algorithm to constant runtime while maintaining high accuracy. This method, known as "Sprint," enables quicker spin matching and prototyping of lattice designs via Bmad.
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA14
About •	Received ※ 30 July 2022 — Revised ※ 09 August 2022 — Accepted ※ 10 August 2022 — Issue date ※ 24 August 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPA29	Machine Learning for Predicting Power Supply Trips in Storage Rings	413
	I. Lobach, M. Borland, G.I. Fystro, A. Sannibale, Y. Sun ANL, Lemont, Illinois, USA A. Diaw, J.P. Edelen RadiaSoft LLC, Boulder, Colorado, USA
	Funding: The work is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357. In the Advanced Photon Source (APS) storage ring at Argonne National Lab, trips in the magnet power supplies (PSs) lead to a complete electron beam loss a few times a year. This results in unexpected interruptions of the users’ experiments. In this contribution, we investigate the historical data for the last two decades to find precursors for the PS trips that could provide an advance notice for future trips and allow some preventive action by the ring operator or by the PS maintenance team. Various unsupervised anomaly detection models can be trained on the vast amounts of available reference data from the beamtime periods that ended with an intentional beam dump. We find that such models can sometimes detect trip precursors in PS currents, voltages, and in the temperatures of magnets, capacitors and transistors (components of PSs).
	Poster TUPA29 [2.116 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA29
About •	Received ※ 03 August 2022 — Revised ※ 07 August 2022 — Accepted ※ 10 August 2022 — Issue date ※ 18 August 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPA55	Progress Toward Improving Accelerator Performance and Automating Operations with Advanced Analysis Software	465
	J.E. Koglin, J.E. Coleman, M. McKerns, D. Ronquillo, A. Scheinker LANL, Los Alamos, New Mexico, USA
	Funding: Research presented in this conference paper was supported by the Laboratory Directed Research and Development program of Los Alamos National Laboratory under project numbers XXG2, XX8R and XXB6. The penetrating radiography provided by the Dual Axis Radiographic Hydrodynamic Test (DARHT) facility is a key capability in executing a core mission of the Los Alamos National Laboratory (LANL). A new suite of software is being developed in the Python programming language to support operations of the of two DARHT linear induction accelerators (LIAs). Historical data, built as hdf5 data structures for over a decade of operations, are being used to develop automated failure and anomaly detection software and train machine learning models to assist in beam tuning. Adaptive machine learning (AML) that incorporate physics-based models are being designed to use non-invasive diagnostic measurements to address the challenge of time variation in accelerator performance and target density evolution. AML methods are also being developed for experiments that use invasive diagnostics to understand the accelerator behavior at key locations, the results of which will be fed back into the accelerator models. The status and future outlook for these developments will be reported, including how Jupyter notebooks are being used to rapidly deploy these advances as highly-interactive web applications.
	Poster TUPA55 [1.919 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA55
About •	Received ※ 15 July 2022 — Revised ※ 08 August 2022 — Accepted ※ 10 August 2022 — Issue date ※ 12 August 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPA83	Derivative-Free Optimization of Multipole Fits to Experimental Wakefield Data	523
	N. Majernik, G. Andonian, W.J. Lynn, J.B. Rosenzweig UCLA, Los Angeles, California, USA P. Piot, T. Xu Northern Illinois University, DeKalb, Illinois, USA
	Funding: Department of Energy DE-SC0017648. A method to deduce the transverse self-wakefields acting on a beam, based only on screen images, is introduced. By employing derivative-free optimization, the relatively high-dimensional parameter space can be efficiently explored to determine the multipole components up to the desired order. This technique complements simulations, which are able to directly infer the wakefield composition. It is applied to representative simulation results as a benchmark and also applied to experimental data on skew wake observations from dielectric slab structures.
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA83
About •	Received ※ 02 August 2022 — Revised ※ 21 August 2022 — Accepted ※ 26 August 2022 — Issue date ※ 11 September 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPA84	Reconstructing Beam Parameters from Betatron Radiation Through Machine Learning and Maximum Likelihood Estimation	527
	S. Zhang, N. Majernik, B. Naranjo, J.B. Rosenzweig, M. Yadav UCLA, Los Angeles, California, USA Ö. Apsimon, C.P. Welsch, M. Yadav The University of Liverpool, Liverpool, United Kingdom
	Funding: US Department of Energy, Division of High Energy Physics, under Contract No. DE-SC0009914. The dense drive beam used in plasma wakefield acceleration generates a linear focusing force that causes electrons inside the witness beam to undergo betatron oscillations, giving rise to betatron radiation. Because information about the properties of the beam is encoded in the betatron radiation, measurements of the radiation such as those recorded by the UCLA-built Compton spectrometer can be used to reconstruct beam parameters. Two possible methods of extracting information about beam parameters from measurements of radiation are machine learning (ML), which is increasingly being implemented for different fields of beam diagnostics, and a statistical technique known as maximum likelihood estimation (MLE). We assess the ability of both machine learning and MLE methods to accurately extract beam parameters from measurements of betatron radiation.
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA84
About •	Received ※ 02 August 2022 — Revised ※ 07 August 2022 — Accepted ※ 10 August 2022 — Issue date ※ 05 October 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEXF1	Accelerator Computation: Fast, Cheap, and Easy
	J.R. Cary Tech-X, Boulder, Colorado, USA
	Computation has seen dramatically increased speed over the past decade, with the rise of device (GPU) computing. To make this increased computing capability more widely available, there is a need to move it to the cloud, where elastic and cheap resources are available. To make this capability more accessible, there is a need for easily used codes, usable after minimal training. This talk presents an approach to all three of these goals, showing that accelerator computations can be made fast, cheap, and easy.
	Slides WEXF1 [6.705 MB]
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPA09	A Parallel Automatic Simulation Tool for Cavity Shape Optimization	634
	L. Ge, Z. Li, C.-K. Ng, L. Xiao SLAC, Menlo Park, California, USA M. Beall, B.R. Downie, O. Klaas Simmetrix Inc., Clifton Park, USA
	Funding: U.S. Department of Energy under contract No. DE-SC0018715. We present a parallel automatic shape optimization workflow for designing accelerator cavities. The newly developed 3D parallel optimization tool Opt3P based on discrete adjoint methods is used to determine the optimal accelerator cavity shape with the desired spectral response. Initial and updated models, meshes, and design velocities of design parameters for defining the cavity shape are generated with Simmetrix tools for mesh generation (MeshSim), geometry modification and query (GeomSim), and user interface tools (SimModeler). Two shape optimization examples using this automatic simulation workflow will be presented here. One is the TESLA cavity with higher-order-mode (HOM) couplers and the other is a superconducting rf (SRF) gun. The objective for the TESLA cavity is to minimize HOM damping factors and for the SRF gun to minimize the surface electric and magnetic fields while maintaining its operating mode frequency at a prescribed value. The results demonstrate that the automatic simulation tool allows an efficient shape optimization procedure with minimal manual operations. All simulations were performed on the NERSC supercomputer Cori system for solution speedup.
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-WEPA09
About •	Received ※ 03 August 2022 — Revised ※ 05 August 2022 — Accepted ※ 09 August 2022 — Issue date ※ 08 October 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPA23	SRF Cavity Instability Detection with Machine Learning at CEBAF	669
	D.L. Turner, R. Bachimanchi, A. Carpenter, J. Latshaw, C. Tennant, L.S. Vidyaratne JLab, Newport News, Virginia, USA
	Funding: Authored by Jefferson Science Associates, LLC under U.S. DOE Contract No. DE-AC05-06OR23177. During the operation of CEBAF, one or more unstable superconducting radio-frequency (SRF) cavities often cause beam loss trips while the unstable cavities themselves do not necessarily trip off. Identifying an unstable cavity out of the hundreds of cavities installed at CEBAF is difficult and time-consuming. The present RF controls for the legacy cavities report at only 1 Hz, which is too slow to detect fast transient instabilities. A fast data acquisition system for the legacy SRF cavities is being developed which samples and reports at 5 kHz to allow for detection of transients. A prototype chassis has been installed and tested in CEBAF. An autoencoder based machine learning model is being developed to identify anomalous SRF cavity behavior. The model is presently being trained on the slow (1 Hz) data that is currently available, and a separate model will be developed and trained using the fast (5 kHz) DAQ data once it becomes available. This paper will discuss the present status of the new fast data acquisition system and results of testing the prototype chassis. This paper will also detail the initial performance metrics of the autoencoder model.
	Poster WEPA23 [1.859 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-WEPA23
About •	Received ※ 01 August 2022 — Revised ※ 04 August 2022 — Accepted ※ 09 August 2022 — Issue date ※ 24 August 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPA24	pyJSPEC - A Python Module for IBS and Electron Cooling Simulation	672
	H. Zhang, S.V. Benson, M.W. Bruker, Y. Zhang JLab, Newport News, Virginia, USA
	Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under contract DE-AC05-06OR23177. The intrabeam scattering is an important collective effect that can deteriorate the property of a high-intensity beam and electron cooling is a method to mitigate the IBS effect. JSPEC (JLab Simulation Package on Electron Cooling) is an open-source C++ program developed at Jefferson Lab, which simulates the evolution of the ion beam under the IBS and/or the electron cooling effect. The Python wrapper of the C++ code, pyJSPEC, for Python 3.x environment has been recently developed and released. It allows the users to run JSPEC simulations in a Python environment. It also makes it possible for JSPEC to collaborate with other accelerator and beam modeling programs as well as plentiful python tools in data visualization, optimization, machine learning, etc. In this paper, we will introduce the features of pyJSPEC and demonstrate how to use it with sample codes and numerical results.
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-WEPA24
About •	Received ※ 02 August 2022 — Revised ※ 08 August 2022 — Accepted ※ 11 August 2022 — Issue date ※ 26 August 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPA29	Real-Time Cavity Fault Prediction in CEBAF Using Deep Learning	687
	M. Rahman, K.M. Iftekharuddin ODU, Norfolk, Virginia, USA A. Carpenter, T.S. McGuckin, C. Tennant, L.S. Vidyaratne JLab, Newport News, Virginia, USA
	Funding: Authored by Jefferson Science Associates, LLC under U.S. DOE Contract No. DE-AC05-06OR23177. Data-driven prediction of future faults is a major research area for many industrial applications. In this work, we present a new procedure of real-time fault prediction for superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF) using deep learning. CEBAF has been afflicted by frequent downtime caused by SRF cavity faults. We perform fault prediction using pre-fault RF signals from C100-type cryomodules. Using the pre-fault signal information, the new algorithm predicts the type of cavity fault before the actual onset. The early prediction may enable potential mitigation strategies to prevent the fault. In our work, we apply a two-stage fault prediction pipeline. In the first stage, a model distinguishes between faulty and normal signals using a U-Net deep learning architecture. In the second stage of the network, signals flagged as faulty by the first model are classified into one of seven fault types based on learned signatures in the data. Initial results show that our model can successfully predict most fault types 200 ms before onset. We will discuss reasons for poor model performance on specific fault types.
	Poster WEPA29 [1.339 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-WEPA29
About •	Received ※ 02 August 2022 — Revised ※ 07 August 2022 — Accepted ※ 11 August 2022 — Issue date ※ 17 August 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPA38	Progress on Machine Learning for the SNS High Voltage Converter Modulators	715
	M.I. Radaideh, S.M. Cousineau, D. Lu ORNL, Oak Ridge, Tennessee, USA T.J. Britton, K. Rajput, M. Schram, L.S. Vidyaratne JLab, Newport News, Virginia, USA G.C. Pappas, J.D. Walden ORNL RAD, Oak Ridge, Tennessee, USA
	The High-Voltage Converter Modulators (HVCM) used to power the klystrons in the Spallation Neutron Source (SNS) linac were selected as one area to explore machine learning due to reliability issues in the past and the availability of large sets of archived waveforms. Progress in the past two years has resulted in generating a significant amount of simulated and measured data for training neural network models such as recurrent neural networks, convolutional neural networks, and variational autoencoders. Applications in anomaly detection, fault classification, and prognostics of capacitor degradation were pursued in collaboration with the Jefferson Laboratory, and early promising results were achieved. This paper will discuss the progress to date and present results from these efforts.
	Poster WEPA38 [1.320 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-WEPA38
About •	Received ※ 25 July 2022 — Revised ※ 08 August 2022 — Accepted ※ 11 August 2022 — Issue date ※ 03 October 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPA40	The L-CAPE Project at FNAL	719
	M. Jain, V.C. Amatya, G.U. Panapitiya, J.F. Strube PNNL, Richland, Washington, USA B.F. Harrison, K.J. Hazelwood, W. Pellico, B.A. Schupbach, K. Seiya, J.M. St. John Fermilab, Batavia, Illinois, USA
	The controls system at FNAL records data asynchronously from several thousand Linac devices at their respective cadences, ranging from 15Hz down to once per minute. In case of downtimes, current operations are mostly reactive, investigating the cause of an outage and labeling it after the fact. However, as one of the most upstream systems at the FNAL accelerator complex, the Linac’s foreknowledge of an impending downtime as well as its duration could prompt downstream systems to go into standby, potentially leading to energy savings. The goals of the Linac Condition Anomaly Prediction of Emergence (L-CAPE) project that started in late 2020 are (1) to apply data-analytic methods to improve the information that is available to operators in the control room, and (2) to use machine learning to automate the labeling of outage types as they occur and discover patterns in the data that could lead to the prediction of outages. We present an overview of the challenges in dealing with time-series data from 2000+ devices, our approach to developing an ML-based automated outage labeling system, and the status of augmenting operations by identifying the most likely devices predicting an outage.
	Poster WEPA40 [1.870 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-WEPA40
About •	Received ※ 03 August 2022 — Revised ※ 12 August 2022 — Accepted ※ 17 August 2022 — Issue date ※ 31 August 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPA70	Tensor Decomposition for the Compression and Analysis of 10 kHz BPM Data	792
	J. Choi, Y. Hidaka, Y. Hu, G.M. Wang BNL, Upton, New York, USA
	Funding: This work is supported in part by the U.S. Department of Energy (DOE) under contract No. DE-SC0012704. In the NSLS-II storage ring during user operation, fast-acquisition (FA) 10-kHz BPM data are collected, and their spectral properties are analyzed. Various periodograms and spectral peaks are being provided every minute, and they are very useful in identifying any changes in the orbit. Unfortunately, because of the large amount of data, only several numbers are being continually archived for later study, and the full raw data are saved only by hand when needed. We are developing methods utilizing tensor decomposition techniques to save and analyze the FA data; this paper reports the current status of this project.
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-WEPA70
About •	Received ※ 02 August 2022 — Revised ※ 08 August 2022 — Accepted ※ 10 August 2022 — Issue date ※ 28 September 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

THXE1	Accelerators for Quantum Technologies
	A.S. Romanenko Fermilab, Batavia, Illinois, USA K.A. Brown BNL, Upton, New York, USA S. Sosa ODU, Norfolk, Virginia, USA
	Dr. A. Romanenko will present "SRF-based accelerator technologies for Quantum" followed by "Large Ion Traps for Quantum Information Systems" presented by Dr. K. Brown. "Examples of AI/ML enabled by HPCs in design applied to a QIS" presentation by Dr. S. Sosa will be followed by panel discussion and audience questions.
	Slides THXE1 [7.433 MB]
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

THXE2	Accelerators for Quantum Technologies
	K.A. Brown BNL, Upton, New York, USA
	Dr. A. Romanenko will present "SRF-based accelerator technologies for Quantum" followed by "Large Ion Traps for Quantum Information Systems" presented by Dr. K. Brown. "Examples of AI/ML enabled by HPCs in design applied to a QIS" presentation by Dr. S. Sosa will be followed by panel discussion and audience questions.
	Slides THXE2 [6.768 MB]
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

THXE3	Accelerators for Quantum Technologies
	S. Sosa ODU, Norfolk, Virginia, USA
	Dr. A. Romanenko will present "SRF-based accelerator technologies for Quantum" followed by "Large Ion Traps for Quantum Information Systems" presented by Dr. K. Brown. "Examples of AI/ML enabled by HPCs in design applied to a QIS" presentation by Dr. S. Sosa will be followed by panel discussion and audience questions.
	Slides THXE3 [1.958 MB]
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

FRXE1	Bayesian Algorithms for Practical Accelerator Control and Adaptive Machine Learning for Time-Varying Systems	921
	A. Scheinker LANL, Los Alamos, New Mexico, USA R.J. Roussel SLAC, Menlo Park, California, USA
	Particle accelerators are complicated machines with thousands of coupled time varying components. The electromagnetic fields of accelerator devices such as magnets and RF cavities drift and are uncertain due to external disturbances, vibrations, temperature changes, and hysteresis. Accelerated charged particle beams are complex objects with 6D phase space dynamics governed by collective effects such as space charge forces, coherent synchrotron radiation, and whose initial phase space distributions change in unexpected and difficult to measure ways. This two-part tutorial presents recent developments in Bayesian methods and adaptive machine learning (ML) techniques for accelerators. Part 1: We introduce Bayesian control algorithms, and we describe how these algorithms can be customized to solve practical accelerator specific problems, including online characterization and optimization. Part 2: We give an overview of adaptive ML (AML) combining adaptive model-independent feedback within physics-informed ML architectures to make ML tools robust to time-variation (distribution shift) and to enable their use further beyond the span of the training data without relying on re-training.
	Slides FRXE1 [34.283 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-FRXE1
About •	Received ※ 08 August 2022 — Revised ※ 10 August 2022 — Accepted ※ 12 August 2022 — Issue date ※ 27 September 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

FRXE2	Bayesian Algorithms for Practical Accelerator Control and Adaptive Machine Learning for Time-Varying Systems
	R.J. Roussel SLAC, Menlo Park, California, USA
	Particle accelerators are complicated machines with thousands of coupled time varying components. The electromagnetic fields of accelerator devices such as magnets and RF cavities drift and are uncertain due to external disturbances, vibrations, temperature changes, and hysteresis. Accelerated charged particle beams are complex objects with 6D phase space dynamics governed by collective effects such as space charge forces, coherent synchrotron radiation, and whose initial phase space distributions change in unexpected and difficult to measure ways. This two-part tutorial presents recent developments in Bayesian methods and adaptive machine learning (ML) techniques for accelerators. Part 1: We introduce Bayesian control algorithms, and we describe how these algorithms can be customized to solve practical accelerator specific problems, including online characterization and optimization. Part 2: We give an overview of adaptive ML (AML) combining adaptive model-independent feedback within physics-informed ML architectures to make ML tools robust to time-variation (distribution shift) and to enable their use further beyond the span of the training data without relying on re-training.
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Realistic CAD-Based Geometries for Arbitrary Magnets with Beam Delivery Simulation (BDSIM)

55

E. Ramoisiaux, R. Dantinne, E. Gnacadja, C. Hernalsteens, S. Musibau, B. Ndihokubwayo, N. Pauly, R. Tesse, M. Vanwelde
ULB, Bruxelles, Belgium
S.T. Boogert, L.J. Nevay, W. Shields
Royal Holloway, University of London, Surrey, United Kingdom
C. Hernalsteens
CERN, Meyrin, Switzerland

Monte Carlo simulations are required to evaluate beam losses and secondary radiation accurately in particle accelerators and beamlines. Detailed CAD geometries are critical to account for a realistic distribution of material masses but increase the model complexity and often lead to code duplication. Beam Delivery Simulation (BDSIM) and the Python package pyg4ometry enable handling such accelerator models within a single, simplified workflow to run complete simulations of primary and secondary particle tracking and interactions with matter using Geant4 routines. Additional capabilities have been developed to model arbitrary bent magnets by associating externally modeled geometries to the magnet poles, yoke, and beampipe. Individual field descriptions can be associated with the yoke and vacuum pipe separately to provide fine-grained control of the magnet model. The implementation of these new features is described in detail and applied to the modeling of the CERN Proton Synchrotron (PS) combined function magnets.

Poster MOPA01 [0.781 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA01

About •

Received ※ 02 August 2022 — Revised ※ 07 August 2022 — Accepted ※ 09 August 2022 — Issue date ※ 16 September 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

MOPA08

Beamline Optimization Methods for High Intensity Muon Beams at PSI

63

E.V. Valetov
PSI, Villigen PSI, Switzerland

Funding: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 884104 (PSI-FELLOW-III-3i).
We perform beamline design optimization for the High Intensity Muon Beams (HIMB) project at the Paul Scherrer Institute (PSI), which will deliver muon beams at the unprecedented rate of 1·10¹⁰ muons/s to next-generation intensity frontier particle physics and material science experiments. For optimization of the design and operational parameters to maximize the beamline transmission, we use the asynchronous Bayesian optimization package DeepHyper and a custom build of G4beamline with variance reduction and measured cross sections. We minimize the beam spot size at the final foci using a COSY INFINITY model with differential-algebraic system knobs, where we minimize the respective transfer map elements using the Levenberg-Marquardt and simulated annealing optimizers. We obtained a transmission of 1.34·10¹⁰ muons/s in a G4beamline model of HIMB’s MUH2 beamline into the experimental area.

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA08

About •

Received ※ 02 August 2022 — Revised ※ 08 August 2022 — Accepted ※ 11 August 2022 — Issue date ※ 23 August 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

MOPA13

Design of a Surrogate Model for MUED at BNL Using VSim, Elegant and HPC

72

S.I. Sosa Guitron, S. Biedron, T.B. Bolin
UNM-ECE, Albuquerque, USA
S. Biedron
Element Aero, Chicago, USA
S. Biedron
UNM-ME, Albuquerque, New Mexico, USA

Funding: U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Program of Electron and Scanning Probe Microscopes, award number DE-SC0021365.
The MeV Ultrafast Electron Diffraction (MUED) instrument at Brookhaven National Laboratory is a unique capability for material science. As part of a plan to make MUED a high-throughput user facility, we are exploring instrumentation developments based on Machine Learning (ML). We are developing a surrogate model of MUED that can be used to support control tasks. The surrogate model will be based on beam simulations that are benchmarked to experimental observations. We use VSim to model the beam dynamics of the radio-frequency gun and Elegant to transport the beam through the rest of the beam-line. We also use High Performance Computing resources from Argonne Leadership Computing Facility to generate the data for the surrogate model based on the original simulation as well as training the ML model.

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA13

About •

Received ※ 01 August 2022 — Revised ※ 09 August 2022 — Accepted ※ 11 August 2022 — Issue date ※ 21 August 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

MOPA18

Residual Dose and Environmental Monitoring for the Fermilab Main Injector Tunnel Using the Data Acquisition Logging Engine (Dale)

87

N. Chelidze, R. Ainsworth, B.C. Brown, D. Capista, K.J. Hazelwood, D.K. Morris, M.J. Murphy
Fermilab, Batavia, Illinois, USA

Funding: Fermi National Accelerator Laboratory
The Recycler and the Main Injector are part of the Fermilab Accelerator complex used to deliver proton beam to the different experiments. It is very important to control and minimize losses in both machines during operation, to reduce personnel dose from residual activation and to preserve component lifetime. To minimize losses, we need to identify the loss points and adjust the components accordingly. The Data Acquisition Loss Engine (DALE) platform has been developed within the Main Injector department and upgraded throughout the years. DALE is used to survey the entire enclosure for residual dose rates and environmental readings when unrestricted access to the enclosure is possible. Currently DALE has two radiation meters, which are aligned along each machine, so loss points can be identified for both at the same time. DALE attaches to the enclosure carts and is continuously in motion monitoring dose rates and other environmental readings. In this paper we will describe how DALE is used to provide radiation maps of the residual dose rates in the enclosure. We will also compare the loss points with the Beam Loss monitor data.

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA18

About •

Received ※ 02 August 2022 — Revised ※ 05 August 2022 — Accepted ※ 11 August 2022 — Issue date ※ 21 September 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

MOPA44

Utilizing Python to Prepare the VENUS Ion Source for Machine Learning

151

A. Kireeff, L. Phair, M.J. Regis, M. Salathe, D.S. Todd
LBNL, Berkeley, California, USA

Funding: This work was supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under Contract No. DE-AC02-05CH11231.
The fully superconducting electron cyclotron resonance (ECR) ion source VENUS is one of the world’s two highest-performing ECR ion sources, and a copy of this source will soon be used to produce ion beams at FRIB. The tuning and optimization of ECR ion sources is time consuming, and there are few detailed theoretical models to guide this work. To aid in this process, we are working toward utilizing machine learning to both efficiently optimize VENUS and reliably maintain its stability for long campaigns. We have created a Python library to interface with the programmable logic controller (PLC) in order to operate VENUS and collect and store source and beam data. We will discuss the design and safety considerations that went into creating this library, the implementation of the library, and some of the capabilities it enables.

Poster MOPA44 [0.862 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA44

About •

Received ※ 17 July 2022 — Revised ※ 27 July 2022 — Accepted ※ 05 August 2022 — Issue date ※ 16 August 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

MOPA55

Facilitating Machine Learning Collaborations Between Labs, Universities, and Industry

164

J.P. Edelen, D.T. Abell, D.L. Bruhwiler, S.J. Coleman, N.M. Cook, A. Diaw, J.A. Einstein-Curtis, C.C. Hall, M.C. Kilpatrick, B. Nash, I.V. Pogorelov
RadiaSoft LLC, Boulder, Colorado, USA
K.A. Brown
BNL, Upton, New York, USA
S. Calder
ORNL RAD, Oak Ridge, Tennessee, USA
A.L. Edelen, B.D. O’Shea, R.J. Roussel
SLAC, Menlo Park, California, USA
C.M. Hoffmann
ORNL, Oak Ridge, Tennessee, USA
E.-C. Huang
LANL, Los Alamos, New Mexico, USA
P. Piot
Northern Illinois University, DeKalb, Illinois, USA
C. Tennant
JLab, Newport News, Virginia, USA

It is clear from numerous recent community reports, papers, and proposals that machine learning is of tremendous interest for particle accelerator applications. The quickly evolving landscape continues to grow in both the breadth and depth of applications including physics modeling, anomaly detection, controls, diagnostics, and analysis. Consequently, laboratories, universities, and companies across the globe have established dedicated machine learning (ML) and data-science efforts aiming to make use of these new state-of-the-art tools. The current funding environment in the U.S. is structured in a way that supports specific application spaces rather than larger collaboration on community software. Here, we discuss the existing collaboration bottlenecks and how a shift in the funding environment, and how we develop collaborative tools, can help fuel the next wave of ML advancements for particle accelerators.

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA55

About •

Received ※ 10 August 2022 — Revised ※ 11 August 2022 — Accepted ※ 22 August 2022 — Issue date ※ 01 September 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

MOPA57

Online Models for X-Ray Beamlines

170

B. Nash, D.T. Abell, M.V. Keilman, P. Moeller, I.V. Pogorelov
RadiaSoft LLC, Boulder, Colorado, USA
Y. Du, A. Giles, J. Lynch, T. Morris, M.S. Rakitin, A. Walter
BNL, Upton, New York, USA
N.B. Goldring
STATE33 Inc., Portland, Oregon, USA

Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Science, under Award Number DE-SC0020593
X-ray beamlines transport synchrotron radiation from the magnetic source to the sample at a synchrotron light source. Alignment of elements such as mirrors and gratings are often done manually and can be quite time consuming. The use of photon beam models during operations is not common in the same way that they are used to great benefit for particle beams in accelerators. Linear and non-linear optics including the effects of coherence may be computed from source properties and augmented with measurements. In collaboration with NSLS-II, we are developing software tools and methods to include the model of the x-ray beam as it passes on its way to the sample. We are integrating the Blue-Sky beamline control toolkit with the Sirepo interface to several x-ray optics codes. Further, we are developing a simplified linear optics approach based on a Gauss-Schell model and linear canonical transforms as well as developing Machine Learning models for use directly from diagnostics data. We present progress on applying these ideas on NSLS-II beamlines and give a future outlook on this rather large and open domain for technological development.

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA57

About •

Received ※ 27 July 2022 — Revised ※ 02 August 2022 — Accepted ※ 07 August 2022 — Issue date ※ 11 August 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

MOPA89

RHIC Electron Beam Cooling Analysis Using Principle Component and Autoencoder Analysis

260

A.D. Tran, Y. Hao
FRIB, East Lansing, Michigan, USA
X. Gu
BNL, Upton, New York, USA

Funding: Work supported by the US Department of Energy under contract No. DE-AC02-98CH10886.
Principal component analysis and autoencoder analysis were used to analyze the experimental data of RHIC operation with low energy RHIC electron cooling (LEReC). This is unsupervised learning which includes electron beam settings and observable during operation. Both analyses were used to gauge the dimensional reducibility of the data and to understand which features are important to beam cooling.

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA89

About •

Received ※ 02 August 2022 — Revised ※ 05 August 2022 — Accepted ※ 06 August 2022 — Issue date ※ 12 August 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUXE1

The Importance of Data, High-Performance Computing, and Artificial Intelligence/Machine Learning

C.M. Sweeney
LANL, Los Alamos, New Mexico, USA
A.L. Edelen
SLAC, Menlo Park, California, USA
D.E. Martin
ANL, Lemont, Illinois, USA

As existing accelerator facilities are upgraded and new facilities come online, data volumes and velocity are increasing even with shorter data collection times. High-performance computing (HPC) systems doing simulation, data analytics and artificial intelligence/machine learning (AI/ML) are playing a major role in pre-experiment planning, design of experiments, real-time beam line and experiment analysis and control, and post-run data processing. Simulation and AI incorporated into experimental data analysis workflows are making efficient use of expensive facilities and accelerating scientific discoveries. HPC is experiencing its own growth, with exascale computers and AI acceleration coming online at several supercomputer centers. AI/ML is in the midst of rapid growth of techniques and expansion into new application areas. This session will focus on current and emerging technologies in HPC, experimental workflows, and AI/ML techniques to help you incorporate them into your own research. Dr. D. Martin will provide "HPC Overview" followed by "Workflows" by Dr. C. Sweeney. "AI and ML" by Dr. A. Edelen will be followed by community discussions and questions from the audience.

Slides TUXE1 [11.946 MB]

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUXE2

High Performance Computing - DOE Facilities, Direction and Applications

D.E. Martin
ANL, Lemont, Illinois, USA

Funding: This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357.
High-performance computing (HPC) systems doing simulation, data analytics and machine learning are playing a major role in accelerator physics, including pre-experiment planning, design of experiments, real-time beam line analysis and control, and post-run data processing. DOE high performance computer user facilities are evolving with new architectures, faster systems and a focus on integrating with experimental facilities.

Slides TUXE2 [16.438 MB]

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUXE3

Artificial Intelligence and Machine Learning for Particle Accelerators

A.L. Edelen
SLAC, Menlo Park, California, USA

As existing accelerator facilities are upgraded and new facilities come online, data volumes and velocity are increasing even with shorter data collection times. High-performance computing (HPC) systems doing simulation, data analytics and artificial intelligence/machine learning (AI/ML) are playing a major role in pre-experiment planning, design of experiments, real-time beam line and experiment analysis and control, and post-run data processing. Simulation and AI incorporated into experimental data analysis workflows are making efficient use of expensive facilities and accelerating scientific discoveries. HPC is experiencing its own growth, with exascale computers and AI acceleration coming online at several supercomputer centers. AI/ML is in the midst of rapid growth of techniques and expansion into new application areas. This session will focus on current and emerging technologies in HPC, experimental workflows, and AI/ML techniques to help you incorporate them into your own research. Dr. D. Martin will provide "HPC Overview" followed by "Workflows" by Dr. C. Sweeney. "AI and ML" by Dr. A. Edelen will be followed by community discussions and questions from the audience.

Slides TUXE3 [17.252 MB]

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUYE1

Coulomb Crystals in Storage Rings for Quantum Information Science

296

K.A. Brown
BNL, Upton, New York, USA
A. Aslam, S. Biedron, T.B. Bolin, C. Gonzalez-Zacarias, S.I. Sosa Guitron
UNM-ECE, Albuquerque, USA
B. Huang
SBU, Stony Brook, USA
T.G. Robertazzi
Stony Brook University, Stony Brook, New York, USA

Quantum information science is a growing field that promises to take computing into a new age of higher performance and larger scale computing as well as being capable of solving problems classical computers are incapable of solving. The outstanding issue in practical quantum computing today is scaling up the system while maintaining interconnectivity of the qubits and low error rates in qubit operations to be able to implement error correction and fault-tolerant operations. Trapped ion qubits offer long coherence times that allow error correction. However, error correction algorithms require large numbers of qubits to work properly. We can potentially create many thousands (or more) of qubits with long coherence states in a storage ring. For example, a circular radio-frequency quadrupole, which acts as a large circular ion trap and could enable larger scale quantum computing. Such a Storage Ring Quantum Computer (SRQC) would be a scalable and fault tolerant quantum information system, composed of qubits with very long coherence lifetimes.

Slides TUYE1 [8.834 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUYE1

About •

Received ※ 17 July 2022 — Revised ※ 02 August 2022 — Accepted ※ 08 August 2022 — Issue date ※ 11 August 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUYE2

Next Generation Computational Tools for the Modeling and Design of Particle Accelerators at Exascale

302

A. Huebl, R. Lehé, C.E. Mitchell, J. Qiang, R.D. Ryne, R.T. Sandberg, J.-L. Vay
LBNL, Berkeley, USA

Funding: Work supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. DOE SC and the NNSA, resources of NERSC, and by LBNL LDRD under DOE Contract DE-AC02-05CH11231.
Particle accelerators are among the largest, most complex devices. To meet the challenges of increasing energy, intensity, accuracy, compactness, complexity and efficiency, increasingly sophisticated computational tools are required for their design and optimization. It is key that contemporary software take advantage of the latest advances in computer hardware and scientific software engineering practices, delivering speed, reproducibility and feature composability for the aforementioned challenges. A new open source software stack is being developed at the heart of the Beam pLasma Accelerator Simulation Toolkit (BLAST) by LBNL and collaborators, providing new particle-in-cell modeling codes capable of exploiting the power of GPUs on Exascale supercomputers. Combined with advanced numerical techniques, such as mesh-refinement, and intrinsic support for machine learning, these codes are primed to provide ultrafast to ultraprecise modeling for future accelerator design and operations.
[1] J.-L. Vay, A. Huebl, et al, Phys. Plasmas 28, 023105 (2021)
[2] J.-L. Vay, A. Huebl, et al, J. Instr. 16, T10003 (2021)
[3] A. Myers, et al (incl. A. Huebl), Parallel Comput. 108, 102833 (2021)

Slides TUYE2 [9.399 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUYE2

About •

Received ※ 13 July 2022 — Revised ※ 02 August 2022 — Accepted ※ 08 August 2022 — Issue date ※ 11 August 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUYE3

An Open-Source Based Data Management and Processing Framework on a Central Server for Scientific Experimental Data

307

A. Liu, J.R. Callahan, S. Poddar, W. Si
Euclid TechLabs, Solon, Ohio, USA
J. Gao
AJS Smartech LLC, Naperville, TX, USA

Funding: This work is supported by the US DOE SBIR program under contract number DE-SC0021512.
The ever-expanding size of accelerator operation and experimental data including those generated by electron microscopes and beamline facilities renders most proprietary software inefficient at managing data. The Findability, Accessibility, Interoperability, and Reuse (FAIR) principles of digital assets require a convenient platform for users to share and manage data on. An open-source data framework for storing raw data and metadata, hosting databases, and providing a platform for data processing and visualization is highly desirable. In this paper, we present an open-source, infrastructure-independent data management software framework, named by Euclid-NexusLIMS, to archive, register, record, visualize and process experimental data. The software was targeted initially for electron microscopes, but can be widely applied to all scientific experimental data.

Slides TUYE3 [5.891 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUYE3

About •

Received ※ 04 August 2022 — Revised ※ 07 August 2022 — Accepted ※ 08 August 2022 — Issue date ※ 24 August 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUYE4

Machine Learning for Anomaly Detection and Classification in Particle Accelerators

311

I. Lobach, M. Borland, K.C. Harkay, N. Kuklev, A. Sannibale, Y. Sun
ANL, Lemont, Illinois, USA

Funding: The work is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357.
We explore the possibility of using a Machine Learning (ML) algorithm to identify the source of occasional poor performance of the Particle Accumulator Ring (PAR) and the Linac-To-PAR (LTP) transport line, which are parts of the injector complex of the Advanced Photon Source (APS) at Argonne National Lab. The cause of reduced injection or extraction efficiencies may be as simple as one parameter being out of range. Still, it may take an expert considerable time to notice it, whereas a well-trained ML model can point at it instantly. In addition, a machine expert might not be immediately available when a problem occurs. Therefore, we began by focusing on such single-parameter anomalies. The training data were generated by creating controlled perturbations of several parameters of PAR and LTP one-by-one, while continuously logging all available process variables. Then, several ML classifiers were trained to recognize certain signatures in the logged data and link them to the sources of poor machine performance. Possible applications of autoencoders and variational autoencoders for unsupervised anomaly detection and for anomaly clustering were considered as well.

Slides TUYE4 [9.534 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUYE4

About •

Received ※ 03 August 2022 — Revised ※ 07 August 2022 — Accepted ※ 08 August 2022 — Issue date ※ 28 August 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUYE5

Multiobjective Optimization of the LCLS-II Photoinjector

N.R. Neveu
SLAC, Menlo Park, California, USA
T.H. Chang, S.T.P. Hudson, J.M. Larson
ANL, Lemont, Illinois, USA
P.L. Franz
Stanford University, Stanford, California, USA

Genetic algorithms and particle swarm optimization are currently the most widely used optimization algorithms in the accelerator physics community. These algorithms can take many evaluations to find optimal solutions for one machine prototype. In this work, the LCLS-II photoinjector is optimized with three optimization algorithms: a genetic algorithm, a surrogate based algorithm, and a multi-start scalarization method. All three algorithms were able to optimize the photoinjector, with various trade-offs for each method discussed here.

Slides TUYE5 [3.919 MB]

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUYE6

High-Fidelity Simulations and Machine Learning for Accelerator Design and Optimization

D. Winklehner
MIT, Cambridge, Massachusetts, USA
A. Adelmann
PSI, Villigen PSI, Switzerland

Funding: NSF #1505858, NSF #162606, NSF #1626069
Computation has become a critically important tool for particle accelerator design and optimization. Thanks to massively parallel codes running on high-performance clusters, we can now accurately predict emergent properties of particle ensembles and non-linear collective effects, and use machine learning (ML) for analysis and to create "virtual twins" of accelerator systems. Here, we will present the IsoDAR experiment in neutrino physics as an example. For it, we have developed a compact and cost-effective cyclotron-based driver to produce very high-intensity beams. The system will be able to deliver cw proton currents of 10 mA on target in the energy regime around 60 MeV. 10 mA is a factor of 10 higher than commercially available machines. This increase in current is possible due to longitudinal-radial coupling through space charge, an effect dubbed "vortex motion". We will discuss the high-fidelity OPAL simulations performed to simulate this effect in the IsoDAR cyclotron and predict beam losses due to halo formation. We will present uncertainty quantification for this design and we will show our study to optimize the IsoDAR injector RFQ using ML.

Slides TUYE6 [2.414 MB]

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUZG1

Magnets to ML to Light Sources: Designing from the Browser with Sirepo

J.P. Edelen, B. Nash
RadiaSoft LLC, Boulder, Colorado, USA
R.J. Roussel
SLAC, Menlo Park, California, USA

Join us for an afternoon intensive with the Sirepo CAE & Design platform! Experts will present their work and lead tutorials using Sirepo’s apps and tools for magnet design, ML techniques in control systems, and X-ray beamline modeling. Bring your own laptop and pre-register to use the free gateway at Sirepo.com for practical exercises. Dr. Edelen will provide a tutorial on how to design dipoles and undulators using parameterized magnets. Dr. Roussel will demonstrate combining the classical Preisach model of hysteresis with ML techniques to efficiently create non-parametric, high-fidelity models of arbitrary systems exhibiting hysteresis. Also shown will be how using these joint hysteresis-beam models allows users to overcome optimization performance limitations when hysteresis effects are ignored. Dr. Nash will review the capabilities of Shadow and SRW within Sirepo, discuss additional optics tools in the interface including brightness and linear optics, demonstrate translation between the two codes, and show how to continue your simulation work in a python based Jupyter notebook.

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPA14

Fast First-Order Spin Propagation for Spin Matching and Polarization Optimization with Bmad

369

J.M. Asimow, G.H. Hoffstaetter, D. Sagan, M.G. Signorelli
Cornell University (CLASSE), Cornell Laboratory for Accelerator-Based Sciences and Education, Ithaca, New York, USA

Accurate spin tracking is essential for the simulation and propagation of polarized beams, in which a majority of the particles’ spin point in the same direction. Bmad, an open-sourced library for the simulation of charged particle dynamics, traditionally tracks spin via integrating through each element of a lattice. While exceptionally accurate, this method has the drawback of being slow; at best, the runtime is proportional to the length of the element. By solving the spin transport equation for simple magnet elements, Bmad can reduce this algorithm to constant runtime while maintaining high accuracy. This method, known as "Sprint," enables quicker spin matching and prototyping of lattice designs via Bmad.

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA14

About •

Received ※ 30 July 2022 — Revised ※ 09 August 2022 — Accepted ※ 10 August 2022 — Issue date ※ 24 August 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPA29

Machine Learning for Predicting Power Supply Trips in Storage Rings

413

I. Lobach, M. Borland, G.I. Fystro, A. Sannibale, Y. Sun
ANL, Lemont, Illinois, USA
A. Diaw, J.P. Edelen
RadiaSoft LLC, Boulder, Colorado, USA

Funding: The work is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357.
In the Advanced Photon Source (APS) storage ring at Argonne National Lab, trips in the magnet power supplies (PSs) lead to a complete electron beam loss a few times a year. This results in unexpected interruptions of the users’ experiments. In this contribution, we investigate the historical data for the last two decades to find precursors for the PS trips that could provide an advance notice for future trips and allow some preventive action by the ring operator or by the PS maintenance team. Various unsupervised anomaly detection models can be trained on the vast amounts of available reference data from the beamtime periods that ended with an intentional beam dump. We find that such models can sometimes detect trip precursors in PS currents, voltages, and in the temperatures of magnets, capacitors and transistors (components of PSs).

Poster TUPA29 [2.116 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA29

About •

Received ※ 03 August 2022 — Revised ※ 07 August 2022 — Accepted ※ 10 August 2022 — Issue date ※ 18 August 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPA55

Progress Toward Improving Accelerator Performance and Automating Operations with Advanced Analysis Software

465

J.E. Koglin, J.E. Coleman, M. McKerns, D. Ronquillo, A. Scheinker
LANL, Los Alamos, New Mexico, USA

Funding: Research presented in this conference paper was supported by the Laboratory Directed Research and Development program of Los Alamos National Laboratory under project numbers XXG2, XX8R and XXB6.
The penetrating radiography provided by the Dual Axis Radiographic Hydrodynamic Test (DARHT) facility is a key capability in executing a core mission of the Los Alamos National Laboratory (LANL). A new suite of software is being developed in the Python programming language to support operations of the of two DARHT linear induction accelerators (LIAs). Historical data, built as hdf5 data structures for over a decade of operations, are being used to develop automated failure and anomaly detection software and train machine learning models to assist in beam tuning. Adaptive machine learning (AML) that incorporate physics-based models are being designed to use non-invasive diagnostic measurements to address the challenge of time variation in accelerator performance and target density evolution. AML methods are also being developed for experiments that use invasive diagnostics to understand the accelerator behavior at key locations, the results of which will be fed back into the accelerator models. The status and future outlook for these developments will be reported, including how Jupyter notebooks are being used to rapidly deploy these advances as highly-interactive web applications.

Poster TUPA55 [1.919 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA55

About •

Received ※ 15 July 2022 — Revised ※ 08 August 2022 — Accepted ※ 10 August 2022 — Issue date ※ 12 August 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPA83

Derivative-Free Optimization of Multipole Fits to Experimental Wakefield Data

523

N. Majernik, G. Andonian, W.J. Lynn, J.B. Rosenzweig
UCLA, Los Angeles, California, USA
P. Piot, T. Xu
Northern Illinois University, DeKalb, Illinois, USA

Funding: Department of Energy DE-SC0017648.
A method to deduce the transverse self-wakefields acting on a beam, based only on screen images, is introduced. By employing derivative-free optimization, the relatively high-dimensional parameter space can be efficiently explored to determine the multipole components up to the desired order. This technique complements simulations, which are able to directly infer the wakefield composition. It is applied to representative simulation results as a benchmark and also applied to experimental data on skew wake observations from dielectric slab structures.

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA83

About •

Received ※ 02 August 2022 — Revised ※ 21 August 2022 — Accepted ※ 26 August 2022 — Issue date ※ 11 September 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPA84

Reconstructing Beam Parameters from Betatron Radiation Through Machine Learning and Maximum Likelihood Estimation

527

S. Zhang, N. Majernik, B. Naranjo, J.B. Rosenzweig, M. Yadav
UCLA, Los Angeles, California, USA
Ö. Apsimon, C.P. Welsch, M. Yadav
The University of Liverpool, Liverpool, United Kingdom

Funding: US Department of Energy, Division of High Energy Physics, under Contract No. DE-SC0009914.
The dense drive beam used in plasma wakefield acceleration generates a linear focusing force that causes electrons inside the witness beam to undergo betatron oscillations, giving rise to betatron radiation. Because information about the properties of the beam is encoded in the betatron radiation, measurements of the radiation such as those recorded by the UCLA-built Compton spectrometer can be used to reconstruct beam parameters. Two possible methods of extracting information about beam parameters from measurements of radiation are machine learning (ML), which is increasingly being implemented for different fields of beam diagnostics, and a statistical technique known as maximum likelihood estimation (MLE). We assess the ability of both machine learning and MLE methods to accurately extract beam parameters from measurements of betatron radiation.

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA84

About •

Received ※ 02 August 2022 — Revised ※ 07 August 2022 — Accepted ※ 10 August 2022 — Issue date ※ 05 October 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEXF1

Accelerator Computation: Fast, Cheap, and Easy

J.R. Cary
Tech-X, Boulder, Colorado, USA

Computation has seen dramatically increased speed over the past decade, with the rise of device (GPU) computing. To make this increased computing capability more widely available, there is a need to move it to the cloud, where elastic and cheap resources are available. To make this capability more accessible, there is a need for easily used codes, usable after minimal training. This talk presents an approach to all three of these goals, showing that accelerator computations can be made fast, cheap, and easy.

Slides WEXF1 [6.705 MB]

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPA09

A Parallel Automatic Simulation Tool for Cavity Shape Optimization

634

L. Ge, Z. Li, C.-K. Ng, L. Xiao
SLAC, Menlo Park, California, USA
M. Beall, B.R. Downie, O. Klaas
Simmetrix Inc., Clifton Park, USA

Funding: U.S. Department of Energy under contract No. DE-SC0018715.
We present a parallel automatic shape optimization workflow for designing accelerator cavities. The newly developed 3D parallel optimization tool Opt3P based on discrete adjoint methods is used to determine the optimal accelerator cavity shape with the desired spectral response. Initial and updated models, meshes, and design velocities of design parameters for defining the cavity shape are generated with Simmetrix tools for mesh generation (MeshSim), geometry modification and query (GeomSim), and user interface tools (SimModeler). Two shape optimization examples using this automatic simulation workflow will be presented here. One is the TESLA cavity with higher-order-mode (HOM) couplers and the other is a superconducting rf (SRF) gun. The objective for the TESLA cavity is to minimize HOM damping factors and for the SRF gun to minimize the surface electric and magnetic fields while maintaining its operating mode frequency at a prescribed value. The results demonstrate that the automatic simulation tool allows an efficient shape optimization procedure with minimal manual operations. All simulations were performed on the NERSC supercomputer Cori system for solution speedup.

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-WEPA09

About •

Received ※ 03 August 2022 — Revised ※ 05 August 2022 — Accepted ※ 09 August 2022 — Issue date ※ 08 October 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPA23

SRF Cavity Instability Detection with Machine Learning at CEBAF

669

D.L. Turner, R. Bachimanchi, A. Carpenter, J. Latshaw, C. Tennant, L.S. Vidyaratne
JLab, Newport News, Virginia, USA

Funding: Authored by Jefferson Science Associates, LLC under U.S. DOE Contract No. DE-AC05-06OR23177.
During the operation of CEBAF, one or more unstable superconducting radio-frequency (SRF) cavities often cause beam loss trips while the unstable cavities themselves do not necessarily trip off. Identifying an unstable cavity out of the hundreds of cavities installed at CEBAF is difficult and time-consuming. The present RF controls for the legacy cavities report at only 1 Hz, which is too slow to detect fast transient instabilities. A fast data acquisition system for the legacy SRF cavities is being developed which samples and reports at 5 kHz to allow for detection of transients. A prototype chassis has been installed and tested in CEBAF. An autoencoder based machine learning model is being developed to identify anomalous SRF cavity behavior. The model is presently being trained on the slow (1 Hz) data that is currently available, and a separate model will be developed and trained using the fast (5 kHz) DAQ data once it becomes available. This paper will discuss the present status of the new fast data acquisition system and results of testing the prototype chassis. This paper will also detail the initial performance metrics of the autoencoder model.

Poster WEPA23 [1.859 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-WEPA23

About •

Received ※ 01 August 2022 — Revised ※ 04 August 2022 — Accepted ※ 09 August 2022 — Issue date ※ 24 August 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPA24

pyJSPEC - A Python Module for IBS and Electron Cooling Simulation

672

H. Zhang, S.V. Benson, M.W. Bruker, Y. Zhang
JLab, Newport News, Virginia, USA

Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under contract DE-AC05-06OR23177.
The intrabeam scattering is an important collective effect that can deteriorate the property of a high-intensity beam and electron cooling is a method to mitigate the IBS effect. JSPEC (JLab Simulation Package on Electron Cooling) is an open-source C++ program developed at Jefferson Lab, which simulates the evolution of the ion beam under the IBS and/or the electron cooling effect. The Python wrapper of the C++ code, pyJSPEC, for Python 3.x environment has been recently developed and released. It allows the users to run JSPEC simulations in a Python environment. It also makes it possible for JSPEC to collaborate with other accelerator and beam modeling programs as well as plentiful python tools in data visualization, optimization, machine learning, etc. In this paper, we will introduce the features of pyJSPEC and demonstrate how to use it with sample codes and numerical results.

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-WEPA24

About •

Received ※ 02 August 2022 — Revised ※ 08 August 2022 — Accepted ※ 11 August 2022 — Issue date ※ 26 August 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPA29

Real-Time Cavity Fault Prediction in CEBAF Using Deep Learning

687

M. Rahman, K.M. Iftekharuddin
ODU, Norfolk, Virginia, USA
A. Carpenter, T.S. McGuckin, C. Tennant, L.S. Vidyaratne
JLab, Newport News, Virginia, USA

Funding: Authored by Jefferson Science Associates, LLC under U.S. DOE Contract No. DE-AC05-06OR23177.
Data-driven prediction of future faults is a major research area for many industrial applications. In this work, we present a new procedure of real-time fault prediction for superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF) using deep learning. CEBAF has been afflicted by frequent downtime caused by SRF cavity faults. We perform fault prediction using pre-fault RF signals from C100-type cryomodules. Using the pre-fault signal information, the new algorithm predicts the type of cavity fault before the actual onset. The early prediction may enable potential mitigation strategies to prevent the fault. In our work, we apply a two-stage fault prediction pipeline. In the first stage, a model distinguishes between faulty and normal signals using a U-Net deep learning architecture. In the second stage of the network, signals flagged as faulty by the first model are classified into one of seven fault types based on learned signatures in the data. Initial results show that our model can successfully predict most fault types 200 ms before onset. We will discuss reasons for poor model performance on specific fault types.

Poster WEPA29 [1.339 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-WEPA29

About •

Received ※ 02 August 2022 — Revised ※ 07 August 2022 — Accepted ※ 11 August 2022 — Issue date ※ 17 August 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPA38

Progress on Machine Learning for the SNS High Voltage Converter Modulators

715

M.I. Radaideh, S.M. Cousineau, D. Lu
ORNL, Oak Ridge, Tennessee, USA
T.J. Britton, K. Rajput, M. Schram, L.S. Vidyaratne
JLab, Newport News, Virginia, USA
G.C. Pappas, J.D. Walden
ORNL RAD, Oak Ridge, Tennessee, USA

The High-Voltage Converter Modulators (HVCM) used to power the klystrons in the Spallation Neutron Source (SNS) linac were selected as one area to explore machine learning due to reliability issues in the past and the availability of large sets of archived waveforms. Progress in the past two years has resulted in generating a significant amount of simulated and measured data for training neural network models such as recurrent neural networks, convolutional neural networks, and variational autoencoders. Applications in anomaly detection, fault classification, and prognostics of capacitor degradation were pursued in collaboration with the Jefferson Laboratory, and early promising results were achieved. This paper will discuss the progress to date and present results from these efforts.

Poster WEPA38 [1.320 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-WEPA38

About •

Received ※ 25 July 2022 — Revised ※ 08 August 2022 — Accepted ※ 11 August 2022 — Issue date ※ 03 October 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPA40

The L-CAPE Project at FNAL

719

M. Jain, V.C. Amatya, G.U. Panapitiya, J.F. Strube
PNNL, Richland, Washington, USA
B.F. Harrison, K.J. Hazelwood, W. Pellico, B.A. Schupbach, K. Seiya, J.M. St. John
Fermilab, Batavia, Illinois, USA

The controls system at FNAL records data asynchronously from several thousand Linac devices at their respective cadences, ranging from 15Hz down to once per minute. In case of downtimes, current operations are mostly reactive, investigating the cause of an outage and labeling it after the fact. However, as one of the most upstream systems at the FNAL accelerator complex, the Linac’s foreknowledge of an impending downtime as well as its duration could prompt downstream systems to go into standby, potentially leading to energy savings. The goals of the Linac Condition Anomaly Prediction of Emergence (L-CAPE) project that started in late 2020 are (1) to apply data-analytic methods to improve the information that is available to operators in the control room, and (2) to use machine learning to automate the labeling of outage types as they occur and discover patterns in the data that could lead to the prediction of outages. We present an overview of the challenges in dealing with time-series data from 2000+ devices, our approach to developing an ML-based automated outage labeling system, and the status of augmenting operations by identifying the most likely devices predicting an outage.

Poster WEPA40 [1.870 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-WEPA40

About •

Received ※ 03 August 2022 — Revised ※ 12 August 2022 — Accepted ※ 17 August 2022 — Issue date ※ 31 August 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPA70

Tensor Decomposition for the Compression and Analysis of 10 kHz BPM Data

792

J. Choi, Y. Hidaka, Y. Hu, G.M. Wang
BNL, Upton, New York, USA

Funding: This work is supported in part by the U.S. Department of Energy (DOE) under contract No. DE-SC0012704.
In the NSLS-II storage ring during user operation, fast-acquisition (FA) 10-kHz BPM data are collected, and their spectral properties are analyzed. Various periodograms and spectral peaks are being provided every minute, and they are very useful in identifying any changes in the orbit. Unfortunately, because of the large amount of data, only several numbers are being continually archived for later study, and the full raw data are saved only by hand when needed. We are developing methods utilizing tensor decomposition techniques to save and analyze the FA data; this paper reports the current status of this project.

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-WEPA70

About •

Received ※ 02 August 2022 — Revised ※ 08 August 2022 — Accepted ※ 10 August 2022 — Issue date ※ 28 September 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

THXE1

Accelerators for Quantum Technologies

A.S. Romanenko
Fermilab, Batavia, Illinois, USA
K.A. Brown
BNL, Upton, New York, USA
S. Sosa
ODU, Norfolk, Virginia, USA

Dr. A. Romanenko will present "SRF-based accelerator technologies for Quantum" followed by "Large Ion Traps for Quantum Information Systems" presented by Dr. K. Brown. "Examples of AI/ML enabled by HPCs in design applied to a QIS" presentation by Dr. S. Sosa will be followed by panel discussion and audience questions.

Slides THXE1 [7.433 MB]

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

THXE2

Accelerators for Quantum Technologies

K.A. Brown
BNL, Upton, New York, USA

Dr. A. Romanenko will present "SRF-based accelerator technologies for Quantum" followed by "Large Ion Traps for Quantum Information Systems" presented by Dr. K. Brown. "Examples of AI/ML enabled by HPCs in design applied to a QIS" presentation by Dr. S. Sosa will be followed by panel discussion and audience questions.

Slides THXE2 [6.768 MB]

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

THXE3

Accelerators for Quantum Technologies

S. Sosa
ODU, Norfolk, Virginia, USA

Dr. A. Romanenko will present "SRF-based accelerator technologies for Quantum" followed by "Large Ion Traps for Quantum Information Systems" presented by Dr. K. Brown. "Examples of AI/ML enabled by HPCs in design applied to a QIS" presentation by Dr. S. Sosa will be followed by panel discussion and audience questions.

Slides THXE3 [1.958 MB]

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

FRXE1

Bayesian Algorithms for Practical Accelerator Control and Adaptive Machine Learning for Time-Varying Systems

921

A. Scheinker
LANL, Los Alamos, New Mexico, USA
R.J. Roussel
SLAC, Menlo Park, California, USA

Particle accelerators are complicated machines with thousands of coupled time varying components. The electromagnetic fields of accelerator devices such as magnets and RF cavities drift and are uncertain due to external disturbances, vibrations, temperature changes, and hysteresis. Accelerated charged particle beams are complex objects with 6D phase space dynamics governed by collective effects such as space charge forces, coherent synchrotron radiation, and whose initial phase space distributions change in unexpected and difficult to measure ways. This two-part tutorial presents recent developments in Bayesian methods and adaptive machine learning (ML) techniques for accelerators. Part 1: We introduce Bayesian control algorithms, and we describe how these algorithms can be customized to solve practical accelerator specific problems, including online characterization and optimization. Part 2: We give an overview of adaptive ML (AML) combining adaptive model-independent feedback within physics-informed ML architectures to make ML tools robust to time-variation (distribution shift) and to enable their use further beyond the span of the training data without relying on re-training.

Slides FRXE1 [34.283 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-FRXE1

About •

Received ※ 08 August 2022 — Revised ※ 10 August 2022 — Accepted ※ 12 August 2022 — Issue date ※ 27 September 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

FRXE2

Bayesian Algorithms for Practical Accelerator Control and Adaptive Machine Learning for Time-Varying Systems

R.J. Roussel
SLAC, Menlo Park, California, USA

Particle accelerators are complicated machines with thousands of coupled time varying components. The electromagnetic fields of accelerator devices such as magnets and RF cavities drift and are uncertain due to external disturbances, vibrations, temperature changes, and hysteresis. Accelerated charged particle beams are complex objects with 6D phase space dynamics governed by collective effects such as space charge forces, coherent synchrotron radiation, and whose initial phase space distributions change in unexpected and difficult to measure ways. This two-part tutorial presents recent developments in Bayesian methods and adaptive machine learning (ML) techniques for accelerators. Part 1: We introduce Bayesian control algorithms, and we describe how these algorithms can be customized to solve practical accelerator specific problems, including online characterization and optimization. Part 2: We give an overview of adaptive ML (AML) combining adaptive model-independent feedback within physics-informed ML architectures to make ML tools robust to time-variation (distribution shift) and to enable their use further beyond the span of the training data without relying on re-training.

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)