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Low Level RF Workshop 2022

9–13 Oct 2022
FHNW Campus Brugg-Windisch
Europe/Zurich timezone

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Session

Machine Learning

3
10 Oct 2022, 13:45
Aula (Building 3)

Aula

Building 3

Conveners

Machine Learning: Machine Learning (2/2)

  • Zheqiao Geng (PSI - Paul Scherrer Institut)

Presentation materials

There are no materials yet.

  1. 93. MACHINE LEARNING BASED SRF CAVITY ACTIVE RESONANCE CONTROL
    Faya Wang
    10/10/2022, 13:45
    Low Level RF Workshop 2022
    Oral

    Motion control for acceleration system is usually very complex, as beam and electromagnetic field may couple with mechanical energy. For example, in SRF cavity, electromagnetic modes are strongly coupled with its mechanical modes via Lorentz-force detuning or external microphonics. Since the coupling is very nonlinear, motion control is usually very challenging, such as resonance control of...

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  2. 23. Machine Learning assisted Cavity Quench Identification at the European XFEL
    Julien Branlard (DESY)
    10/10/2022, 14:15
    Low Level RF Workshop 2022
    Oral

    A server-based quench detection system is used since the beginning of operation at the European XFEL (2017) to stop driving superconducting cavities if they experience a quench. While this approach effectively detects quenches, it also generates false positives, tripping the accelerating stations when failures other than quenches occur. Using the post-mortem data snapshots generated for every...

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  3. 5. FPGA-based hardware acceleration of machine learning algorithms for particle accelerators
    Gianluca Martino (TUHH, DESY)
    10/10/2022, 14:35
    Low Level RF Workshop 2022
    Oral

    Diagnosis and supervision of particle accelerators is mostly a manual task, requiring deep insight by human operators. The usage of machine learning and data analysis has the potential to enhance the controllability and the diagnosis capability.
    However, applications like longitudinal phase-space estimation, automatic control optimization, or anomaly detection can be used only when the...

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  4. 63. Applications of Machine Learning for RF Systems
    Jonathan Edelen (RadiaSoft)
    10/10/2022, 14:55
    Low Level RF Workshop 2022
    Oral

    The application of machine learning to accelerators has been a dinner table discussion amongst members of the community with an ever increasing list of application spaces. ML has successfully been applied to the improvement of diagnostics, on-line modeling, anomaly detection, and postmortem data analysis. When it comes to accelerator RF systems, machine learning has been of most interest for...

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