Keyword: quadrupole
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WEPV011 Research on Correction of Beam Beta Function of HLS-II Storage Ring Based on Deep Learning network, storage-ring, controls, feedback 645
 
  • Y.B. Yu, C. Li, W. Li, G. Liu, W. Xu, K. Xuan
    USTC/NSRL, Hefei, Anhui, People’s Republic of China
 
  The beam stability of the storage ring determines the light quality of synchrotron radiation. The beam stability of the storage ring will be affected by many factors ’such as magnetic field error, installation error, foundation vibration, temperature variation, etc., so it is inevitable to correct the beam optical parameters to improve the beam stability. In this paper, the deep learning technology is used to establish the HLS-II storage ring beam stability model, and the beam optical parameters can be corrected based on the model. The simulation results show that this method realizes the simulation correction of the Beta function of the HLS-II storage ring, and the correction accuracy precision meets the design requirements.  
poster icon Poster WEPV011 [2.142 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-WEPV011  
About • Received ※ 09 October 2021       Revised ※ 15 November 2021       Accepted ※ 17 November 2021       Issue date ※ 21 November 2021
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WEPV023 Development of a Smart Alarm System for the CEBAF Injector operation, network, vacuum, solenoid 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 ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-WEPV023  
About • Received ※ 10 October 2021       Accepted ※ 19 January 2022       Issue date ※ 14 March 2022  
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THAL04 Machine Learning Based Tuning and Diagnostics for the ATR Line at BNL network, simulation, controls, diagnostics 803
 
  • J.P. Edelen, K. Bruhwiler, E.G. Carlin, C.C. Hall
    RadiaSoft LLC, Boulder, Colorado, USA
  • K.A. Brown, V. Schoefer
    BNL, Upton, New York, 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.
Over the past several years machine learning has increased in popularity for accelerator applications. We have been exploring the use of machine learning as a diagnostic and tuning tool for transfer line from the AGS to RHIC at Brookhaven National Laboratory. In our work, inverse models are used to either provide feed-forward corrections for beam steering or as a diagnostic to illuminate quadrupole magnets that have excitation errors. In this talk we present results on using machine learning for beam steering optimization for a range of different operating energies. We also demonstrate the use of inverse models for optical error diagnostics. Our results are from studies that use combine simulation and measurement data.
 
slides icon Slides THAL04 [4.845 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-THAL04  
About • Received ※ 10 October 2021       Revised ※ 22 October 2021       Accepted ※ 06 February 2022       Issue date ※ 01 March 2022
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