Keyword: controls
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MOPA15 Synchronous High-Frequency Distributed Readout for Edge Processing at the Fermilab Main Injector and Recycler distributed, real-time, Ethernet, operation 79
 
  • J.R. Berlioz, J.M.S. Arnold, M.R. Austin, P.M. Hanlet, K.J. Hazelwood, M.A. Ibrahim, A. Narayanan, D.J. Nicklaus, G. Pradhan, A.L. Saewert, B.A. Schupbach, R.M. Thurman-Keup, N.V. Tran
    Fermilab, Batavia, Illinois, USA
  • J. Jiang, H. Liu, S. Memik, R. Shi, M. Thieme, D. Ulusel
    Northwestern University, Evanston, Illinois, USA
  • A. Narayanan
    Northern Illinois University, DeKalb, Illinois, USA
  
  Funding: Operated by Fermi Research Alliance, LLC under Contract No.De-AC02-07CH11359 with the United States Department of Energy. Additional funding provided by Grant Award No. LAB 20-2261
The Main Injector (MI) was commissioned using data acquisition systems developed for the Fermilab Main Ring in the 1980s. New VME-based instrumentation was commissioned in 2006 for beam loss monitors (BLM), which provided a more systematic study of the machine and improved displays of routine operation. However, current projects are demanding more data and at a faster rate from this aging hardware. One such project, Real-time Edge AI for Distributed Systems (READS), requires the high-frequency, low-latency collection of synchronized BLM readings from around the approximately two-mile accelerator complex. Significant work has been done to develop new hardware to monitor the VME backplane and broadcast BLM measurements over Ethernet, while not disrupting the existing operations-critical functions of the BLM system. This paper will detail the design, implementation, and testing of this parallel data pathway. 		 
poster icon Poster MOPA15 [1.641 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA15  
About • Received ※ 03 August 2022 — Revised ※ 04 August 2022 — Accepted ※ 14 August 2022 — Issue date ※ 19 August 2022
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)  
 
MOPA25 Simulated Lorentz Force Detuning Compensation with a Double Lever Tuner on a Dressed ILC/1.3 GHz Cavity at Room Temperature cavity, flattop, resonance, SRF 106
 
  • C. Contreras-Martinez, Y.M. Pischalnikov, J.C. Yun
    Fermilab, Batavia, Illinois, USA
  
  Pulsed SRF linacs with high accelerating gradients experience large frequency shifts caused by Lorentz force detuning (LFD). A piezoelectric actuator with a resonance control algorithm can maintain the cavity frequency at the nominal level thus reducing the RF power. This study uses a double lever tuner with a piezoelectric actuator for compensation and another piezoelectric actuator to simulate the effects of the Lorentz force pulse. A double lever tuner has an advantage by increasing the stiffness of the cavity-tuner system thus reducing the effects of LFD. The tests are conducted at room temperature and with a dressed 1.3 GHz 9-cell cavity.  
poster icon Poster MOPA25 [0.931 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA25  
About • Received ※ 03 August 2022 — Revised ※ 09 August 2022 — Accepted ※ 11 August 2022 — Issue date ※ 13 August 2022
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MOPA29 Second Generation Fermilab Main Injector 8 GeV Beamline Collimation Preliminary Design collimation, vacuum, shielding, simulation 116
 
  • K.J. Hazelwood, P. Adamson, B.C. Brown, D. Capista, R.M. Donahue, B.L. Klein, N.V. Mokhov, V.S. Pronskikh, V.I. Sidorov, M.C. Vincent
    Fermilab, Batavia, Illinois, USA
  
  The current Fermilab Main Injector 8 GeV beamline transverse collimation system was installed in 2006. Since then, proton beam intensities and rates have increased significantly. With the promise of even greater beam intensities and a faster repetition rate when the PIP-II upgrade completes later this decade, the current collimation system will be insufficient. Over the past 18 months, multiple collimation designs have been investigated, some more traditional and others novel. A preliminary design review was conducted and a design chosen. Work is underway to finalize the chosen design, prototype some of its novel components and procure parts for installation Summer 2023.  
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA29  
About • Received ※ 03 August 2022 — Revised ※ 08 August 2022 — Accepted ※ 15 August 2022 — Issue date ※ 25 September 2022
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MOPA41 Diagnostics for LINAC Optimization with Machine Learning linac, DTL, network, diagnostics 139
 
  • R.V. Sharankova, M.W. Mwaniki, K. Seiya, M.E. Wesley
    Fermilab, Batavia, Illinois, USA
  
  The Fermilab Linac delivers 400 MeV H− beam to the rest of the accelerator chain. Providing stable intensity, energy, and emittance is key since it directly affects downstream machines. To operate high current beam, accelerators must minimize uncontrolled particle loss; this is generally accomplished by minimizing beam emittance. Ambient temperature and humidity variations are known to affect resonance frequency of the accelerating cavities which induces emittance growth. In addition, the energy and phase space distribution of particles emerging from the ion source are subject to fluctuations. To counter these effects we are working on implementing dynamic longitudinal parameter optimization based on Machine Learning (ML). As an input for the ML model, signals from beam diagnostic have to be well understand and reliable. We have been revisiting diagnostics in the linac. In this presentation we discuss the status of the diagnostics and beam studies as well as the status and plans for ML-based optimization.  
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA41  
About • Received ※ 05 August 2022 — Accepted ※ 06 August 2022 — Issue date ※ 07 September 2022  
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MOPA43 Dee Voltage Regulator for the 88-Inch Cyclotron cyclotron, feedback, detector, power-supply 147
 
  • M. Kireeff, P. Bloemhard, T. Hassan, L. Phair
    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
A new broadband Dee voltage regulator was designed and built for the 88-Inch Cyclotron at Lawrence Berkeley National Laboratory. The previous regulator was obsolete, consequently, it was difficult to troubleshoot and repair. Additionally, during operation, it displayed problems of distortion and stability at certain frequencies. The new regulator uses off-the-shelf components that can detect and disable the RF during sparking events, protecting the RF driver system. Furthermore, it improves the tuning of the cyclotron and allows consistency in operation. 		 
poster icon Poster MOPA43 [1.032 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA43  
About • Received ※ 02 August 2022 — Revised ※ 04 August 2022 — Accepted ※ 16 August 2022 — Issue date ※ 09 September 2022
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MOPA44 Utilizing Python to Prepare the VENUS Ion Source for Machine Learning ion-source, PLC, interface, ECR 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 icon 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
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MOPA55 Facilitating Machine Learning Collaborations Between Labs, Universities, and Industry simulation, software, operation, framework 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
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MOPA57 Online Models for X-Ray Beamlines optics, emittance, synchrotron, radiation 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
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MOPA66 Hadron Monitor Calibration System for NuMI hadron, software, proton, target 193
 
  • N.L. Muldrow
    IIT, Chicago, Illinois, USA
  • P. Snopok
    Illinois Institute of Technology, Chicago, Illlinois, USA
  • K. Yonehara
    Fermilab, Batavia, Illinois, USA
  
  Funding: CAST Fellowship
NuMI (Neutrinos at Main Injector) beamline at Fermi National Accelerator Laboratory provides neutrinos to various neutrino experiments. The hadron monitor consisting of a 5 by 5 array of ionization chambers is part of the diagnostics for the beamline. In order to calibrate the hadron monitor, a gamma source is needed. We present the status and progress of the development of the calibration system for the hadron monitor. The system based on Raspberry Pi controlled CNC system, motors, and position sensors would allow us to place the gamma source precisely to calibrate the signal gain of individual pixels. The ultimate outcome of the study is a prototype of the calibration system. 		 
poster icon Poster MOPA66 [2.300 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA66  
About • Received ※ 18 July 2022 — Accepted ※ 12 August 2022 — Issue date ※ 05 September 2022  
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MOPA75 Machine Learning for Slow Spill Regulation in the Fermilab Delivery Ring for Mu2e extraction, quadrupole, experiment, target 214
 
  • A. Narayanan
    Northern Illinois University, DeKalb, Illinois, USA
  • J.M.S. Arnold, M.R. Austin, J.R. Berlioz, P.M. Hanlet, K.J. Hazelwood, M.A. Ibrahim, V.P. Nagaslaev, D.J. Nicklaus, G. Pradhan, P.S. Prieto, A.L. Saewert, B.A. Schupbach, K. Seiya, R.M. Thurman-Keup, N.V. Tran
    Fermilab, Batavia, Illinois, USA
  • J. Jiang, H. Liu, S. Memik, R. Shi, M. Thieme, D. Ulusel
    Northwestern University, Evanston, Illinois, USA
  
  Funding: Work done partly (READS) collaboration at Fermilab (Grant Award No. LAB 20-2261). Fermilab is managed by Fermi Research Alliance, LLC (FRA), acting under Contract No. DE-AC02-07CH11359.
A third-integer resonant slow extraction system is being developed for the Fermilab’s Delivery Ring to deliver protons to the Mu2e experiment. During a slow extraction process, the beam on target is liable to experience small intensity variations due to many factors. Owing to the experiment’s strict requirements in the quality of the spill, a Spill Regulation System (SRS) is currently under design. The SRS primarily consists of three components - slow regulation, fast regulation, and harmonic content tracker. In this presentation, we shall present the investigations of using Machine Learning (ML) in the fast regulation system, including further optimizations of PID controller gains for the fast regulation, prospects of an ML agent completely replacing the PID controller using supervised learning schemes such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) ML models, the simulated impact and limitation of machine response characteristics on the effectiveness of both PID and ML regulation of the spill. We also present here nascent results of Reinforcement Learning efforts, including continuous-action soft actor-critic methods, to regulate the spill rate. 		 
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA75  
About • Received ※ 03 August 2022 — Revised ※ 08 August 2022 — Accepted ※ 18 September 2022 — Issue date ※ 05 October 2022
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MOPA78 Temporally-Shaped Ultraviolet Pulses for Tailored Bunch Generation at Argonne Wakefield Accelerator electron, laser, wakefield, cathode 222
 
  • T. Xu, P. Piot
    Northern Illinois University, DeKalb, Illinois, USA
  • S. Carbajo
    UCLA, Los Angeles, California, USA
  • S. Carbajo, R.A. Lemons
    SLAC, Menlo Park, California, USA
  • P. Piot
    ANL, Lemont, Illinois, USA
  
  Photocathode laser shaping is an appealing technique to generate tailored electron bunches due to its versatility and simplicity. Most photocathodes require photon energies exceeding the nominal photon energy produced by the lasing medium. A common setup consists of an infrared (IR) laser system with nonlinear frequency conversion to the ultraviolet (UV). In this work, we present the numerical modeling of a temporal shaping technique capable of producing electron bunches with linearly-ramped current profiles for application to collinear wakefield accelerators. Specifically, we show that controlling higher-order dispersion terms associated with the IR pulse provides some control over the UV temporal shape. Beam dynamics simulation of an electron-bunch shaping experiment at the Argonne Wakefield Accelerator is presented.  
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA78  
About • Received ※ 01 August 2022 — Revised ※ 06 August 2022 — Accepted ※ 09 August 2022 — Issue date ※ 31 August 2022
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MOPA84 Superconducting Cavity Commissioning for the FRIB Linac cavity, MMI, cryomodule, linac 242
 
  • W. Chang, W. Hartung, S.H. Kim, T. Konomi, S.R. Kunjir, J.T. Popielarski, K. Saito, T. Xu, S. Zhao
    FRIB, East Lansing, Michigan, USA
  
  The superconducting driver linac for the Facility for Rare Isotope Beams (FRIB) is a heavy ion accelerator that has 46 cryomodules with 324 superconducting (SC) cavities that accelerate ions to 200 MeV per nucleon. Linac commissioning was done in multiple phases, in parallel with technical installation. Ion beam have now been accelerated to the design energy through the full linac; rare isotopes were first produced in December 2021; and the first user experiment was completed in May 2022. All cryomodules were successfully commissioned. Cryomodule commissioning included establishing the desired cavity fields, measuring field emission X-rays, optimizing the tuner control loops, measuring the cavity dynamic heat load, and confirming the low-level RF control (amplitude and phase stability). Results on cryomodule commissioning and cryomodule performance will be presented.  
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA84  
About • Received ※ 13 July 2022 — Revised ※ 02 August 2022 — Accepted ※ 13 August 2022 — Issue date ※ 05 September 2022
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MOPA88 FRIB and UEM LLRF Controller Upgrade LLRF, cavity, FPGA, operation 256
 
  • S.R. Kunjir, E. Bernal, D.G. Morris, S. Zhao
    FRIB, East Lansing, Michigan, USA
  • C.-Y. Ruan
    MSU, East Lansing, Michigan, USA
  
  Funding: Supported by the U.S. DOE Office of Science under Cooperative Agreement DE-SC0000661, the State of Michigan, Michigan State University and U.S. National Science Foundation grant DMR-1625181.
The Facility for Rare Isotope Beams (FRIB) is developing a 644 MHz superconducting (SC) cavity for a future upgrade project. The current low level radio frequency (LLRF) controller at FRIB is not able to operate at 644 MHz. The Ultrafast Electron Microscope (UEM) laboratory within the Department of Physics at Michigan State University designed an LLRF controller based on analog RF components to operate a 1.013 GHz room temperature (RT) cavity. With requirements for improved stability, performance and user controls there was a need to upgrade the analog LLRF controller. The FRIB radio frequency (RF) group designed, developed and fabricated a new digital LLRF controller, with high-speed serial interface between system on chip field programmable gate array and fast data converters and capable of high frequency direct sampling, to meet the requirements of 644 MHz SC cavity and 1.013 GHz UEM RT cavity. This paper gives an overview of the upgraded digital LLRF controller, its features, improvements and preliminary test results. 		 
poster icon Poster MOPA88 [2.818 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA88  
About • Received ※ 01 August 2022 — Revised ※ 03 August 2022 — Accepted ※ 04 August 2022 — Issue date ※ 16 August 2022
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MOPA90 Relating Initial Distribution to Beam Loss on the Front End of a Heavy-Ion Linac Using Machine Learning network, simulation, LEBT, emittance 263
 
  • A.D. Tran, Y. Hao
    FRIB, East Lansing, Michigan, USA
  • J.L. Martinez Marin, B. Mustapha
    ANL, Lemont, Illinois, USA
  
  Funding: This work was supported by a sub-reward from Argonne National Laboratory and supported by the U.S. Department of Energy, under Contract No. DE-AC02-06CH11357.
This work demonstrates using a Neural Network and a Gaussian Process to model the ATLAS front-end. Various neural network architectures were created and trained on the machine settings and outputs to model the phase space projections. The model was then trained on a dataset, with non-linear distortion, to gauge the transferability of the model from simulation to machine. 		 
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA90  
About • Received ※ 02 August 2022 — Revised ※ 05 August 2022 — Accepted ※ 06 August 2022 — Issue date ※ 11 September 2022
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TUYE1 Coulomb Crystals in Storage Rings for Quantum Information Science laser, storage-ring, rfq, operation 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 icon 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
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TUYE4 Machine Learning for Anomaly Detection and Classification in Particle Accelerators network, injection, linac, operation 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 icon 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
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TUPA13 Affordable, Efficient Injection-Locked Magnetrons for Superconducting Cavities cavity, injection, electron, GUI 366
 
  • M. Popovic, M.A. Cummings, R.P. Johnson, S.A. Kahn, R.R. Lentz, M.L. Neubauer, T. Wynn
    Muons, Inc, Illinois, USA
  • T. Blassick, J.K. Wessel
    Richardson Electronics Ltd, Lafox, Illinois, USA
  
  Funding: DE-SC0022586.
Existing magnetrons that are typically used to study methods of control or lifetime improvements for SRF accelerators are built for much different applications such kitchen microwave ovens (1kW, 2.45 GHz) or industrial heating (100 kW, 915 MHz). In this project, Muons, Inc. will work with an industrial partner to develop fast and flexible manufacturing techniques to allow many ideas to be tested for construction variations that enable new phase and amplitude injection locking control methods, longer lifetime, and inexpensive refurbishing resulting in the lowest possible life-cycle costs. In Phase II magnetron sources will be tested on SRF cavities to accelerate an electron beam at JLab. A magnetron operating at 650 MHz will be constructed and tested with our novel patented subcritical voltage operation methods to drive an SRF cavity. The choice of 650 MHz is an optimal frequency for magnetron efficiency. The critical areas of magnetron manufacturing and design affecting life-cycle costs that will be modeled for improvement include: Qext, filaments, magnetic field, vane design, and novel control of outgassing. 		 
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA13  
About • Received ※ 05 August 2022 — Revised ※ 11 August 2022 — Accepted ※ 12 August 2022 — Issue date ※ 23 August 2022
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TUPA15 Development of a CVD System for Next-Generation SRF Cavities cavity, SRF, GUI, vacuum 372
 
  • G. Gaitan, P. Bishop, A.T. Holic, G. Kulina, J. Sears, Z. Sun
    Cornell University (CLASSE), Cornell Laboratory for Accelerator-Based Sciences and Education, Ithaca, New York, USA
  • M. Liepe
    Cornell University, Ithaca, New York, USA
  • B.W. Wendland
    University of Minnesota, Minnesota, USA
  
  Funding: This research is funded by the National Science Foundation under Grant No. PHY-1549132, the Center for Bright Beams.
Next-generation, thin-film surfaces employing Nb3Sn, NbN, NbTiN, and other compound superconductors are destined to allow reaching superior RF performance levels in SRF cavities. Optimized, advanced deposition processes are required to enable high-quality films of such materials on large and complex-shaped cavities. For this purpose, Cornell University is developing a remote plasma-enhanced chemical vapor deposition (CVD) system that facilitates coating on complicated geometries with a high deposition rate. This system is based on a high-temperature tube furnace with a clean vacuum and furnace loading system. The use of plasma alongside reacting precursors will significantly reduce the required processing temperature and promote precursor decomposition. The system can also be used for annealing cavities after the CVD process to improve the surface layer. The chlorine precursors have the potential to be corrosive to the equipment and pose specific safety concerns. A MATLAB GUI has been developed to control and monitor the CVD system at Cornell. 		 
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA15  
About • Received ※ 14 July 2022 — Revised ※ 08 August 2022 — Accepted ※ 09 August 2022 — Issue date ※ 22 August 2022
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)  
 
TUPA34 Model-Based Calibration of Control Parameters at the Argonne Wakefield Accelerator network, gun, simulation, wakefield 427
 
  • I.P. Sugrue, B. Mustapha, P. Piot, J.G. Power
    ANL, Lemont, Illinois, USA
  • N. Krislock
    Northern Illinois University, DeKalb, Illinois, USA
  
  Particle accelerators utilize a large number of control parameters to generate and manipulate beams. Digital models and simulations are often used to find the best operating parameters to achieve a set of given beam parameters. Unfortunately, the optimized physics parameters cannot precisely be set in the control system due to, e.g., calibration uncertainties. We developed a data-driven physics-informed surrogate model using neural networks to replace digital models relying on beam-dynamics simulations. This surrogate model can then be used to perform quick diagnostics of the Argonne Wakefield accelerator in real time using nonlinear least-squares methods to find the most likely operating parameters given a measured beam distribution.  
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA34  
About • Received ※ 05 August 2022 — Revised ※ 09 August 2022 — Accepted ※ 10 August 2022 — Issue date ※ 24 September 2022
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)  
 
TUPA41 Applications of Machine Learning in Photo-Cathode Injectors laser, electron, cathode, network 441
 
  • A. Aslam
    UNM-ECE, Albuquerque, USA
  • M. Babzien
    BNL, Upton, New York, USA
  • S. Biedron
    Element Aero, Chicago, USA
  
  To configure a photoinjector to reproduce a given electron bunch with the desired characteristics, it is necessary to adjust the operating parameters with high precision. More or less, the fine tunability of the laser parameters are of extreme importance as we try to model further applications of the photoinjector. The laser pulse incident on the photocathode critically affects the electron bunch 3D phase space. Parameters such as the laser pulse transverse shape, total energy, and temporal profile must be controlled independently, any laser pulse variation over both short and long-time scales also requires correction. The ability to produce arbitrary laser intensity distributions enables better control of electron bunch transverse and longitudinal emittance by affecting the space-charge forces throughout the bunch. In an accelerator employing a photoinjector, electron optics in the beamline downstream are used to transport, manipulate, and characterize the electron bunch. The adjustment of the electron optics to achieve a desired electron bunch at the interaction point is a much better understood problem than laser adjustment, so this research emphasizes laser shaping.  
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA41  
About • Received ※ 30 July 2022 — Revised ※ 12 August 2022 — Accepted ※ 13 August 2022 — Issue date ※ 07 September 2022
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)  
 
TUPA43 Novel RF Phase Detector for Accelerator Applications detector, LLRF, cavity, feedback 446
 
  • J.M. Potter
    JP Accelerator Works, Los Alamos, New Mexico, USA
  
  A novel phase detector has been developed that is suitable for use in an rf phase locked loop for locking an rf source to an rf accelerator structure or phase locking the accelerator structure to a fixed or adjustable frequency rf source. It is also useful for fast phase feedback to control the phase of an accelerator rf field. The principle is applicable to a wide range of frequencies and amplitudes. The phase is uniquely and unambiguously determined over 360°, eliminating the need for external phase shifters or phase references. The operation of this phase detector is described in detail. An application is described that uses a DDS-based LLRF source as the rf input to a high-power rf system.  
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA43  
About • Received ※ 02 August 2022 — Revised ※ 04 August 2022 — Accepted ※ 05 August 2022 — Issue date ※ 06 October 2022
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)  
 
TUPA47 Upgrade from ADCs with Centrally Scheduled Triggers to Continually Triggered Waveform Digitizers EPICS, FPGA, experiment, timing 452
 
  • S.A. Baily, B.C. Atencio, A.J. Braido, C.D. Hatch, J.O. Hill, S.M. Johnson, L.S. Kennel, M. Pieck, L.E. Walker, H.A. Watkins, E.E. Westbrook, K. Xu, D.D. Zimmermann
    LANL, Los Alamos, New Mexico, USA
  
  The Los Alamos Neutron Science Center (LANSCE) control system includes many data channels that are timed and flavored, i.e., users can specify the species of beam and time within the beam pulse at which data are reported. The legacy LANSCE control system accom-plished this task by queuing up application software-initiated requests and scheduling Analog to Digital Con-verter (ADC) readout with custom programmable time-delay gated and multiplexed Remote Information and Control Equipment (RICE). This year we upgraded this system to a new Experimental Physics and Industrial Control System (EPICS) system that includes signal ded-icated waveform digitizer. An appropriate subset of the data is then returned as specified by each client. This is made possible by improvements to EPICS software, a Commercial Off-The-Shelf (COTS) Field Programmable Gate Array (FPGA) Mezzanine Card (FMC) based ADC and a COTS VPX FPGA card with EPICS embedded on a soft-core processor. This year we upgraded over 1200 waveform channels from RICE to the new TDAQ (Timed/flavored Data Acquisition) system.
LA-UR-22-27932 		 
poster icon Poster TUPA47 [1.379 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA47  
About • Received ※ 02 August 2022 — Revised ※ 08 August 2022 — Accepted ※ 10 August 2022 — Issue date ※ 05 October 2022
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TUPA58 Iterative Tuning of the Beam Feedforward Controller for LANSCE LINAC Digital Low Level RF Control System beam-loading, cavity, LLRF, neutron 475
 
  • S. Kwon, A.T. Archuleta, L.J. Castellano, M.S. Prokop, C. Rose, P.A. Torrez, P. Van Rooy
    LANL, Los Alamos, New Mexico, USA
  
  Funding: USDOE
This paper addresses an iterative particle beam phase and amplitude feedforward controller tuning method based on the gradient search approach. The method does not need an a priori plant model as it only needs data collected in previous experimental runs. The controller is implemented on a field programmable gate array (FPGA) equipped with a real-time operating system and a network connection. Data from each RF pulse is collected and sent via the network to the FPGA for processing. The controller tuning is performed between the RF pulses. Once the tuning is performed, the controller parameters are downloaded to the controller in the FPGA and new controller parameters are applied at the upcoming RF pulse 		 
poster icon Poster TUPA58 [0.998 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA58  
About • Received ※ 01 August 2022 — Revised ※ 09 August 2022 — Accepted ※ 10 August 2022 — Issue date ※ 07 September 2022
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)  
 
TUPA59 RF System Upgrade for Low Energy DTL Cavity at LANSCE DTL, LLRF, cavity, MMI 478
 
  • J.T.M. Lyles, R.E. Bratton, T.W. Hall, M. Sanchez Barrueta
    LANL, Los Alamos, New Mexico, USA
  
  Funding: Work supported by the United States Department of Energy, National Nuclear Security Agency, under contract 89233218CNA000001.
The Los Alamos Neutron Science Center (LANSCE) 100-MeV Drift Tube Linac (DTL) uses four accelerating cavities. In May of 2021, a new RF amplifier system was commissioned to drive the first 4-MeV cavity. It had been powered for 30 years with a triode vacuum tube RF amplifier driven by a tetrode, along with four more vacuum tubes for anode high-voltage modulation. The new amplifier system uses one tetrode amplifier driven by a 20-kW solid state amplifier (SSA) to generate 400 kWp at 201.25 MHz. The tetrode amplifier is protected for reflected power from the DTL by a coaxial circulator. The new installation includes cRio controls and a fast protection and monitoring system capable of reacting to faults within 10 µs. A new digital low-level RF (LLRF) system has been installed that integrates I/Q signal processing, PI feedback, and feedforward controls for beam loading compensation. Issues with LLRF stability were initially encountered due to interaction from thermal-related RF phase changes. After these issues were solved, the final outcome has been a reliable new RF system to complete the overall upgrade of the LANSCE DTL RF power plant. 		 
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA59  
About • Received ※ 03 August 2022 — Revised ※ 04 August 2022 — Accepted ※ 06 August 2022 — Issue date ※ 12 August 2022
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)  
 
TUPA62 LANSCE Control System’s 50th Anniversary EPICS, timing, network, data-acquisition 482
 
  • M. Pieck, C.D. Hatch, J.O. Hill, H.A. Watkins, E.E. Westbrook
    LANL, Los Alamos, New Mexico, USA
  
  After almost exactly 50 years in service, the LANSCE (Los Alamos Neutron Science Center) control system has achieved a major milestone, replacing its original and reliable RICE (Remote Instrumentation and Control Equipment) with a modern customized control system. The task of replacing RICE was challenging because of its technology (late 1960’s), number of channels (>10,000), unique characteristics (all-modules data takes, timed/flavored data takes) and that it was designed as an integral part of the whole accelerator. We discuss the history, RICE integral architecture, upgrade efforts, and the new system providing cutting-edge capabilities. The boundary condition was that upgrades only could be implemented during the annual four-month accelerator maintenance outage. This led to a multi-phased project which turned out to be about an 11-year effort.  
poster icon Poster TUPA62 [1.985 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA62  
About • Received ※ 02 August 2022 — Revised ※ 09 August 2022 — Accepted ※ 10 August 2022 — Issue date ※ 25 September 2022
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)  
 
TUPA65 Machine Learning for the LANL Electromagnetic Isotope Separator dipole, ion-source, feedback, electron 490
 
  • A. Scheinker, K.W. Dudeck, C.P. Leibman
    LANL, Los Alamos, New Mexico, USA
  
  Funding: Los Alamos National Laboratory Electromagnetic Isotope Separator Project.
The Los Alamos National Laboratory electromagnetic isotope separator (EMIS) utilizes a Freeman ion source to generate beams of various elements which are accelerated to 40 keV and passed through a 75-degree bend using a large dipole magnet with a radius of 1.2 m. The isotope mass differences translate directly to a spread in momentum, dp, relative to the design momentum p0. Momentum spread is converted to spread in the horizontal arrival location dx at a target chamber by the dispersion of the dipole magnet: dx = D(s)dp/p0. By placing a thin slit leading to a collection chamber at a location xc specific isotope mass is isolated by adjusting the dipole magnet strength or the beam energy. The arriving beam current at xc is associated with average isotope atomic mass, giving an isotope mass spectrum I(m) measured in mA. Although the EMIS is a compact system (5 m) setting up and automatically running at an optimal isotope separation profile I(m) profile is challenging due to time-variation of the complex source as well as un-modeled disturbances. We present preliminary results of developing adaptive machine learning-based tools for the EMIS beam and for the accelerator components. 		 
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA65  
About • Received ※ 18 July 2022 — Revised ※ 07 August 2022 — Accepted ※ 08 August 2022 — Issue date ※ 10 August 2022
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)  
 
TUPA69 Improving Cavity Phase Measurements at Los Alamos Neutron Science Center cavity, GUI, LLRF, neutron 493
 
  • P. Van Rooy, A.T. Archuleta, L.J. Castellano, S. Kwon, M.S. Prokop, P.A. Torrez
    LANL, Los Alamos, New Mexico, USA
  
  Control stability of the phase and amplitude in the cavity is a significant contributor to beam performance. The ability to measure phase and amplitude of pulsed RF systems at accuracies of ± 0.1 degrees and ± 0.1 percent required for our systems is difficult, and custom-designed circuitry is required. The digital low-level RF upgrade at the Los Alamos Neutron Science Center is continuing to progress with improved cavity phase measurements. The previous generation of the cavity phase and amplitude measurement system has a phase ambiguity, which requires repeated calibrations to ascertain the correct phase direction. The new phase measurement system removes the ambiguity and the need for field calibration while improving the range and precision of the cavity phase measurements. In addition, the new digital low-level RF systems is designed to upgrade the legacy system without significant mechanical, electrical, or cabling changes. Performance data for the new phase measurement system is presented.  
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA69  
About • Received ※ 02 August 2022 — Revised ※ 11 August 2022 — Accepted ※ 21 August 2022 — Issue date ※ 08 September 2022
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)  
 
WEZD1 ARDAP’s Perspective on Accelerator Technology R&D in the U.S. operation, electron, collider, laser 592
 
  • B.E. Carlsten, E.R. Colby, R.A. Marsh, M. White
    ARDAP, Washington, USA
  
  DOE operates several particle accelerator facilities and is planning several new forward-leaning accelerator facilities over the next decade or two. These new facilities will focus on discovery science research and fulfilling other core DOE missions. Near and mid-term examples include PIP-II and FACET-II (for High Energy Physics); LCLS-II, SNS-PPU, APS-U, and ALS-U (for Basic Energy Sciences); FRIB (for Nuclear Physics); NSTX-U and MPEX (for Fusion Energy Sciences); and Scorpius (for NNSA). Longer-term examples may include future colliders, the SNS-STS, LCLS-II HE, and EIC. In addition to domestic facilities, DOE’s Office of Science (SC) also contributes to several international efforts. Together, these new facilities constitute a multibillion-dollar construction and operations investment. To be successful, they will require advances in state-of-the-art accelerator technologies. They will also require the National Laboratories to procure a variety of accelerator components. This paper summarizes how DOE is working to address these upcoming R&D and accelerator component production needs through its new office of Accelerator R&D and Production (ARDAP).  
slides icon Slides WEZD1 [2.310 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-WEZD1  
About • Received ※ 05 August 2022 — Revised ※ 09 August 2022 — Accepted ※ 11 August 2022 — Issue date ※ 19 August 2022
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WEZD3 Magnetron R&D Progress for High Efficiency CW RF Sources of Industrial Accelerators injection, power-supply, feedback, experiment 597
 
  • H. Wang, K. Jordan, R.M. Nelson, S.A. Overstreet, R.A. Rimmer
    JLab, Newport News, Virginia, USA
  • J.N. Blum
    VCU, Richmond, Virginia, USA
  • B.R.L. Coriton, C.P. Moeller, K.A. Thackston
    GA, San Diego, California, USA
  • J.L. Vega
    The College of William and Mary, Williamsburg, Virginia, USA
  • G. Ziemyte
    UKY, Kentucky, USA
  
  Funding: Authored by Jefferson Science Associates, LLC under U.S. DOE Contract No. DE-AC05-06OR23177, and DOE OS/HEP Accelerator Stewardship award 2019-2022.
After the demonstration of using high efficiency magnetron power to combine and aim to drive a radio frequency accelerator at 2450MHz in CW mode [1], we have used trim coils adding to a water-cooled magnetron and three amplitude modulation methods in an open-loop control to further suppress the 120Hz side-band noise to -46.7dBc level. We have also successfully demonstrated the phase-locking to an industrial grade cooking magnetron transmitter at 915MHz with a 75kW CW power delivered to a water load by using a -26.6dBc injection signal. The sideband noise at 360Hz from the 3-Phase SCRs DC power supply can be reduced to -16.2dBc level. Their power combing scheme and higher power application to industrial accelerators are foreseeing.
[1] H. Wang, et al, Magnetron R&D for High Efficiency CW RF Sources for Industrial Accelerators, TUPAB348, 12th Int. Particle Acc. Conf. IPAC2021, Campinas, SP, Brazil. 		 
slides icon Slides WEZD3 [3.074 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-WEZD3  
About • Received ※ 18 July 2022 — Revised ※ 25 July 2022 — Accepted ※ 08 August 2022 — Issue date ※ 11 August 2022
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)  
 
WEPA19 HE Production Update at JLab - Introducing an Enhanced Nitrogen Purge for Clean String Assembly cavity, cryomodule, vacuum, hardware 659
 
  • P.D. Owen
    JLab, Newport News, Virginia, USA
  
  A major limitation to cryomodule performance is field emission caused by particulates within the superconducting cavities. To reduce contamination of the inner surfaces during assembly in a cleanroom, the whole string can be connected to a purge system, which maintains a constant overpressure of dry, clean nitrogen gas. Following successes of similar systems at XFEL and Fermilab, Jefferson Lab followed this example for the production of LCLS-II HE cryomodules. Implementing this system required new procedures, infrastructure, and hardware, as well as significant testing of the system before production began. This paper will summarize the implemented controls and procedures, including lessons learned from Fermilab, as well as the results of mock-up tests. Based on the latter, the system was used to assemble the first article string in April 2022, and was also used during a rework required due to issues with cold FPC ceramics two months later. The benefits of using a purge system with regards to procedure, time savings, and added flexibility for potential rework have already proven to provide a significant improvement for the production of LCLS-II-HE cryomodules at Jefferson Lab.  
poster icon Poster WEPA19 [1.538 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-WEPA19  
About • Received ※ 02 August 2022 — Revised ※ 08 August 2022 — Accepted ※ 11 August 2022 — Issue date ※ 21 August 2022
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)  
 
WEPA23 SRF Cavity Instability Detection with Machine Learning at CEBAF cavity, EPICS, SRF, linac 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 icon 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)  
 
WEPA40 The L-CAPE Project at FNAL operation, linac, network, alignment 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 icon 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)  
 
WEPA55 Applications of Machine Automation with Robotics and Computer Vision in Cleanroom Assemblies SRF, cavity, operation, vacuum 756
 
  • A. Liu, J.R. Callahan, E. Gomez, S.M. Milller, W. Si
    Euclid TechLabs, Solon, Ohio, USA
  
  Funding: This work is supported by the US DOE SBIR program under contract number DE-SC0021736.
Modern linear particle accelerators use superconducting radio frequency (SRF) cavities for achieving extremely high-quality factors (Q) and higher beam stability. The assembly process of the system, although with a much more stringent cleanness requirement, is very similar to the ultrahigh vacuum (UHV) system operation procedure. Humans, who are conventionally the operators in this procedure, can only avoid contaminating the system by wearing proper sterile personal protection equipment to avoid direct skin contact with the systems, or dropping particulates. However, humans unavoidably make unintentional mistakes that can contaminate the environment: cross contamination of the coverall suits during wearing, slippage of masks or goggles, damaged gloves, and so forth. Besides, humans are limited when operating heavy weights, which may lead to incorrect procedures, or even worse, injury. In this paper, we present our recent work on a viable and cost-effective machine automation system composed of a robotic arm and a computer vision system for the assembly process in a cleanroom environment, for example for SRF string assemblies, and more. 		 
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-WEPA55  
About • Received ※ 30 July 2022 — Revised ※ 04 August 2022 — Accepted ※ 06 August 2022 — Issue date ※ 12 August 2022
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)  
 
WEPA63 Extensions of the Complex (IQ) Baseband RF Cavity Model Including RF Source and Beam Interactions cavity, simulation, beam-loading, linac 767
 
  • S.P. Jachim, B.J. Cook, J.R.S. Falconer
    Arizona State University, Tempe, USA
  
  Funding: This work was supported in part by NSF award #1935994.
This paper extends prior work describing a complex envelope (i.e., baseband) dynamic model of excited accelerator RF cavities, including the effects of frequency detuning, beam loading, reflections, multiple drive ports, and parasitic modes. This model is presented here in closed-form transfer function and state-variable realizations, which may be more appropriate for analytic purposes. Several example simulations illustrate the detailed insight into RF system behavior afforded by this model. 		 
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-WEPA63  
About • Received ※ 28 July 2022 — Revised ※ 08 August 2022 — Accepted ※ 10 August 2022 — Issue date ※ 15 August 2022
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)  
 
WEPA64 Design and Commissioning of the ASU CXLS Machine Protection System GUI, klystron, detector, machine-protect 770
 
  • S.P. Jachim, B.J. Cook, J.R.S. Falconer, A.J. Gardeck, W.S. Graves, M.R. Holl, R.S. Rednour, D.M. Smith, J.V. Vela
    Arizona State University, Tempe, USA
  
  Funding: This work was supported in part by NSF award #1935994.
To protect against fault conditions in the high-power RF transport and accelerating structures of the Arizona State University (ASU) Compact X-Ray Light Source (CXLS), the Machine Protection System (MPS) extinguishes the 6.5-MW RF energy sources within approximately 50 ns of the fault event. In addition, each fault is localized and reported remotely via USB for operational and maintenance purposes. This paper outlines the requirements, design, and performance of the MPS applied on the CXLS. 		 
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-WEPA64  
About • Received ※ 13 July 2022 — Revised ※ 28 July 2022 — Accepted ※ 08 August 2022 — Issue date ※ 12 August 2022
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)  
 
WEPA76 Radio Frequency System of the NSLS-II Injector LINAC for Multi-Bunch-Mode Beams linac, klystron, beam-loading, operation 813
 
  • H. Ma, J. Rose, C. Sorrentino
    BNL, Upton, New York, USA
  
  Funding: US DOE, Office of BES
The Multi-Bunch Mode (MBM) beam injection opera-tion of NSLS-II LINAC requires a beam-loading compen-sation for its rf field. That requirement has a significant impact on its radio frequency system (RF), in both the low-level rf control and the high-power klystron transmit-ters. Specifically, for the rf control, it requires the output vector modulation have enough bandwidth to be able to respond the transients by the MBM beam of 40~300 nS long. For the high-power rf transmitters, it requires the klystrons to operate in a near-linear region to be able to respond the linear rf control for the beam-loading compensation, which means a need of ~30% extra rf power overhead, compared to the single-bunch mode operations. The digital signal processing and the network configuration for the rf controllers are also the important areas in the implementation. The original system design was driven by the MBM beam operation requirements, and our system upgrade today continues to be guided by the same principles. 		 
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-WEPA76  
About • Received ※ 03 August 2022 — Revised ※ 09 August 2022 — Accepted ※ 10 August 2022 — Issue date ※ 24 August 2022
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THXD2 6D Phase Space Diagnostics Based on Adaptive Tuning of the Latent Space of Encoder-Decoder Convolutional Neural Networks solenoid, feedback, network, electron 837
 
  • A. Scheinker
    LANL, Los Alamos, New Mexico, USA
  
  We present a general approach to 6D phase space diagnostics for charged particle beams based on adaptively tuning the low-dimensional latent space of generative encoder-decoder convolutional neural networks (CNN). Our approach first trains the CNN based on supervised learning to learn the correlations and physics constrains within a given accelerator system. The input of the CNN is a high dimensional collection of 2D phase space projections of the beam at the accelerator entrance together with a vector of accelerator parameters such as magnet and RF settings. The inputs are squeezed down to a low-dimensional latent space from which we generate the output in the form of projections of the beam’s 6D phase space at various accelerator locations. After training the CNN is applied in an unsupervised adaptive manner by comparing a subset of the output predictions to available measurements with the error guiding feedback directly in the low-dimensional latent space. We show that our approach is robust to unseen time-variation of the input beam and accelerator parameters and a study of the robustness of the method to go beyond the span of the training data.  
slides icon Slides THXD2 [19.086 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-THXD2  
About • Received ※ 18 July 2022 — Revised ※ 05 August 2022 — Accepted ※ 08 August 2022 — Issue date ※ 09 August 2022
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THXD4 Online Accelerator Tuning with Adaptive Bayesian Optimization photon, MMI, toolkit, software-tool 842
 
  • N. Kuklev, M. Borland, G.I. Fystro, H. Shang, 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.
Particle accelerators require continuous adjustment to maintain beam quality. At the Advanced Photon Source (APS) this is accomplished using a mix of operator-controlled and automated tools. To improve the latter, we explored the use of machine learning (ML) at the APS injector complex. The core approach we chose was Bayesian optimization (BO), which is well suited for sparse data tasks. To enable long-term online use, we modified BO into adaptive Bayesian optimization (ABO) though auxiliary models of device drift, physics-informed quality and constraint weights, time-biased data subsampling, digital twin retraining, and other approaches. ABO allowed for compensation of changes in inputs and objectives without discarding previous data. Benchmarks showed better ABO performance in several simulated and experimental cases. To integrate ABO into the operational workflow, we developed a Python command line utility, pysddsoptimize, that is compatible with existing Tcl/Tk tools and the SDDS data format. This allowed for fast implementation, debugging, and benchmarking. Our results are an encouraging step for the wider adoption of ML at APS. 		 
slides icon Slides THXD4 [4.797 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-THXD4  
About • Received ※ 01 August 2022 — Revised ※ 08 August 2022 — Accepted ※ 10 August 2022 — Issue date ※ 08 October 2022
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THZE2 Developing Control System Specifications and Requirements for Electron Ion Collider electron, instrumentation, operation, software 901
 
  • A. Blednykh, D.M. Gassner
    Brookhaven National Laboratory (BNL), Electron-Ion Collider, Upton, New York, USA
  • E.C. Aschenauer, P. Baxevanis, M. Blaskiewicz, K.A. Drees, T. Hayes, J.P. Jamilkowski, G.J. Marr, S. Nemesure, V. Schoefer, T.C. Shrey, K.S. Smith, F.J. Willeke
    BNL, Upton, New York, USA
  • L.R. Dalesio
    EPIC Consulting, Medford, New York, USA
  
  An Accelerator Research facility is a unique science and engineering challenge in that the requirements for developing a robust, optimized science facility are limited by engineering and cost limitations. Each facility is planned to achieve some science goal within a given schedule and budget and is then expected to operate for three decades. In three decades, the mechanical systems and the industrial IO to control them is not likely to change. In that same time, electronics will go through some 4 generations of change. The software that integrates the systems and provides tools for operations, automation, data analysis and machine studies will have many new standards. To help understand the process of designing and planning such a facility, we explain the specifications and requirements for the Electron Ion Collider (EIC) from both a physics and engineering perspective.  
slides icon Slides THZE2 [5.375 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-THZE2  
About • Received ※ 04 August 2022 — Revised ※ 10 August 2022 — Accepted ※ 11 August 2022 — Issue date ※ 13 September 2022
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THZE3 An Electrodeless Diamond Beam Monitor electron, experiment, detector, vacuum 904
 
  • S.V. Kuzikov, P.V. Avrakhov, C.-J. Jing, E.W. Knight
    Euclid TechLabs, Solon, Ohio, USA
  • D.S. Doran, C.-J. Jing, J.G. Power, E.E. Wisniewski
    ANL, Lemont, Illinois, USA
  • C.-J. Jing
    Euclid Beamlabs, Bolingbrook, USA
  
  Funding: The work was supported by DoE SBIR grant #DE-SC0019642.
Being a wide-band semiconductor, diamond can be used to measure the flux of passing particles based on a particle-induced conductivity effect. We recently demonstrated a diamond electrodeless electron beam halo monitor. That monitor was based on a thin piece of diamond (blade) placed in an open high-quality microwave resonator. The blade partially intercepted the beam. By measuring the change in RF properties of the resonator, one could infer the beam parameters. At Argonne Wakefield Accelerator we have tested 1D and 2D monitors. To enhance the sensitivity of our diamond sensor, we proposed applying a bias voltage to the diamond which can sustain the avalanche of free carriers. In experiment carried out with 120 kV, ~1 µA beam we showed that the response signal for the avalanche monitor biased with up to 5 kV voltage can be up to 100 times larger in comparison with the signal of the same non-biased device. 		 
slides icon Slides THZE3 [4.257 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-THZE3  
About • Received ※ 20 July 2022 — Revised ※ 28 July 2022 — Accepted ※ 06 August 2022 — Issue date ※ 08 August 2022
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FRXE1 Bayesian Algorithms for Practical Accelerator Control and Adaptive Machine Learning for Time-Varying Systems network, feedback, experiment, electron 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 icon 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
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