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    Item type:Publication, Access status: with files ,
    Impact of seismic activities on the optical synchronization system of the European X-ray free-electron laser
    (Cambridge University Press (CUP), 2025-03-05)
    Grünhagen, Arne
    ;
    Schütte, Maximilian
    ;
    Lamb, Thorsten
    ;
    Schulz, Sebastian
    ;
    Eichler, Annika
    This study investigates the influence of seismic activities on the optical synchronization system of the European X-ray Free-Electron Laser. We analyze the controller I/O data of phase-locked-loops in length-stabilized links, focusing on the response to earthquakes, ocean-generated microseism and civilization noise. By comparing the controller data with external data, we were able to identify disturbances and their effects on the control signals. Our results show that seismic events influence the stability of the phase-locked loops. Even earthquakes that are approximately 5000 km away cause remarkable fluctuations in the in-loop control signals. Ocean-generated microseism in particular has an enormous influence on the in-loop control signals due to its constant presence. The optical synchronization system is so highly sensitive that it can even identify vibrations caused by civilization, such as road traffic or major events like concerts or sport events. The phase-locked loops manages to eliminate more than 99 % of the existing interference.
    Publicationtype: Journal Article
      24  41
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    Item type:Publication, Access status: with files ,
    Reinforcement learning-trained optimisers and Bayesian optimisation for online particle accelerator tuning
    (Springer, 2024-12-01)
    Kaiser, Jan
    ;
    Xu, Chenran
    ;
    Eichler, Annika
    ;
    Santamaria Garcia, Andrea
    ;
    Stein, Oliver
    Online tuning of particle accelerators is a complex optimisation problem that continues to require manual intervention by experienced human operators. Autonomous tuning is a rapidly expanding field of research, where learning-based methods like Bayesian optimisation (BO) hold great promise in improving plant performance and reducing tuning times. At the same time, reinforcement learning (RL) is a capable method of learning intelligent controllers, and recent work shows that RL can also be used to train domain-specialised optimisers in so-called reinforcement learning-trained optimisation (RLO). In parallel efforts, both algorithms have found successful adoption in particle accelerator tuning. Here we present a comparative case study, assessing the performance of both algorithms while providing a nuanced analysis of the merits and the practical challenges involved in deploying them to real-world facilities. Our results will help practitioners choose a suitable learning-based tuning algorithm for their tuning tasks, accelerating the adoption of autonomous tuning algorithms, ultimately improving the availability of particle accelerators and pushing their operational limits.
    Publicationtype: Journal Article
      34  122
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    Item type:Publication, Access status: without files ,
    Influence of environmental parameters on calibration drift in superconducting RF cavities
    (2024-01-01)
    Sun, Y.
    ;
    Bellanda, A.
    ;
    Branlard, J.
    ;
    Richter, B.
    ;
    Eichler, Annika
    Precisely calibrating superconducting Radio-Frequency (RF) linear accelerators is crucial for accurately assessing cavity bandwidth and detuning, which provides valuable insights into cavity performance, facilitates optimal accelerator operation, and enables effective fault detection and diagnosis. In practice, however, calibration of RF signals can present several challenges, with calibration drift being a significant issue, especially in settings prone to humidity and temperature fluctuations. In this paper, we delve into the effect of environmental factors on the calibration drift of superconducting RF cavities. Specifically, we examine long-term calibration drifts and explore how environmental variables such as humidity, temperature, and environmental noise affect this phenomenon. The results show that environmental factors, particularly relative humidity, significantly influence calibration drifts. By analyzing these correlations, appropriate compensation algorithms can be designed to mitigate and eliminate these effects, thus optimizing calibration accuracy and stability.
    Publicationtype: Conference Paper
      5
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    Item type:Publication, Access status: with files ,
    Learning to do or learning while doing: reinforcement learning and bayesian optimisation for online continuous tuning
    (2023-06-06)
    Kaiser, Jan
    ;
    Xu, Chenran
    ;
    Eichler, Annika
    ;
    Santamaria Garcia Andrea
    ;
    Stein, Oliver
    Online tuning of real-world plants is a complex optimisation problem that continues to require manual intervention by experienced human operators. Autonomous tuning is a rapidly expanding field of research, where learning-based methods, such as Reinforcement Learning-trained Optimisation (RLO) and Bayesian optimisation (BO), hold great promise for achieving outstanding plant performance and reducing tuning times. Which algorithm to choose in different scenarios, however, remains an open question. Here we present a comparative study using a routine task in a real particle accelerator as an example, showing that RLO generally outperforms BO, but is not always the best choice. Based on the study's results, we provide a clear set of criteria to guide the choice of algorithm for a given tuning task. These can ease the adoption of learning-based autonomous tuning solutions to the operation of complex real-world plants, ultimately improving the availability and pushing the limits of operability of these facilities, thereby enabling scientific and engineering advancements.
    Publicationtype: Preprint
      34  33