Keyword: solenoid
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WEPV023 Development of a Smart Alarm System for the CEBAF Injector operation, network, vacuum, quadrupole 691
  • D.T. Abell, J.P. Edelen
    RadiaSoft LLC, Boulder, Colorado, USA
  • B.G. Freeman, R. Kazimi, D.G. Moser, C. Tennant
    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 Award Number DE-SC0019682.
RadiaSoft and Jefferson Laboratory are working together to develop a machine-learning-based smart alarm system for the CEBAF injector. Because of the injector’s large number of parameters and possible fault scenarios, it is highly desirable to have an autonomous alarm system that can quickly identify and diagnose unusual machine states. We present our work on artificial neural networks designed to identify such undesirable machine states. In particular, we test both auto-encoders and inverse models as possible tools for differentiating between normal and abnormal states. These models are being developed using both supervised and unsupervised learning techniques, and are being trained using CEBAF injector data collected during dedicated machine studies as well as during regular operations. Lastly, we discuss tradeoffs between the two types of models.
DOI • reference for this paper ※  
About • Received ※ 10 October 2021       Accepted ※ 19 January 2022       Issue date ※ 14 March 2022  
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THAL02 Bayesian Techniques for Accelerator Characterization and Control experiment, target, controls, simulation 791
  • R.J. Roussel, A.L. Edelen, C.E. Mayes
    SLAC, Menlo Park, California, USA
  • J.P. Gonzalez-Aguilera, Y.K. Kim
    University of Chicago, Chicago, Illinois, USA
  Funding: This work was supported by the U.S. National Science Foundation under Award No. PHY-1549132, the Center for Bright Beams.
Accelerators and other large experimental facilities are complex, noisy systems that are difficult to characterize and control efficiently. Bayesian statistical modeling techniques are well suited to this task, as they minimize the number of experimental measurements needed to create robust models, by incorporating prior, but not necessarily exact, information about the target system. Furthermore, these models inherently take into account noisy and/or uncertain measurements and can react to time-varying systems. Here we will describe several advanced methods for using these models in accelerator characterization and optimization. First, we describe a method for rapid, turn-key exploration of input parameter spaces using little-to-no prior information about the target system. Second, we highlight the use of Multi-Objective Bayesian optimization towards efficiently characterizing the experimental Pareto front of a system. Throughout, we describe how unknown constraints and parameter modification costs are incorporated into these algorithms.
slides icon Slides THAL02 [4.453 MB]  
DOI • reference for this paper ※  
About • Received ※ 10 October 2021       Revised ※ 10 November 2021       Accepted ※ 21 November 2021       Issue date ※ 26 December 2021
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