Author: Tennant, C.
Paper Title Page
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
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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 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
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WEPA25 Field Emission Mitigation in CEBAF SRF Cavities Using Deep Learning 676
  • K. Ahammed, J. Li
    ODU, Norfolk, Virginia, USA
  • A. Carpenter, R. Suleiman, C. Tennant, L.S. Vidyaratne
    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 Continuous Electron Beam Accelerator Facility (CEBAF) operates hundreds of superconducting radio frequency (SRF) cavities in its two main linear accelerators. Field emission can occur when the cavities are set to high operating RF gradients and is an ongoing operational challenge. This is especially true in newer, higher gradient SRF cavities. Field emission results in damage to accelerator hardware, generates high levels of neutron and gamma radiation, and has deleterious effects on CEBAF operations. So, field emission reduction is imperative for the reliable, high gradient operation of CEBAF that is required by experimenters. Here we explore the use of deep learning architectures via multilayer perceptron to simultaneously model radiation measurements at multiple detectors in response to arbitrary gradient distributions. These models are trained on collected data and could be used to minimize the radiation production through gradient redistribution. This work builds on previous efforts in developing machine learning (ML) models, and is able to produce similar model performance as our previous ML model without requiring knowledge of the field emission onset for each cavity.
poster icon Poster WEPA25 [1.586 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-WEPA25  
About • Received ※ 01 August 2022 — Revised ※ 03 August 2022 — Accepted ※ 05 August 2022 — Issue date ※ 20 September 2022
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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 icon 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
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