Keyword: software
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MOPA55 Facilitating Machine Learning Collaborations Between Labs, Universities, and Industry controls, simulation, 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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MOPA66 Hadron Monitor Calibration System for NuMI hadron, controls, 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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TUYE2 Next Generation Computational Tools for the Modeling and Design of Particle Accelerators at Exascale simulation, GPU, space-charge, plasma 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 icon 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
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TUYE3 An Open-Source Based Data Management and Processing Framework on a Central Server for Scientific Experimental Data framework, experiment, data-management, network 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 icon 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
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TUPA55 Progress Toward Improving Accelerator Performance and Automating Operations with Advanced Analysis Software diagnostics, operation, cathode, electron 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 icon 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
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WEYE4 Electron Cloud Simulations in the Fermilab Recycler electron, simulation, proton, optics 581
  • A.P. Schreckenberger
    University of Illinois at Urbana-Champaign, Urbana, USA
  • R. Ainsworth
    Fermilab, Batavia, Illinois, USA
  We present a simulation study to characterize the stability region of the Fermilab Recycler Ring in the context of secondary emission yield (SEY). Interactions between electrons and beam pipe material can produce electron clouds that jeopardize beam stability in certain focusing configurations. Such an instability was documented in the Recycler, and the work presented here reflects improvements to better understand that finding. We incorporated the Furman-Pivi Model into a PyECLOUD analysis, and we determined the instability threshold given various bunch lengths, beam intensities, SEY magnitudes, and model parameters.  
slides icon Slides WEYE4 [2.096 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-WEYE4  
About • Received ※ 01 August 2022 — Revised ※ 06 August 2022 — Accepted ※ 08 August 2022 — Issue date ※ 30 September 2022
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THZE2 Developing Control System Specifications and Requirements for Electron Ion Collider controls, electron, instrumentation, operation 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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