Author: Baker, K.R.L.
Paper Title Page
TUPOST048 Development of a Virtual Diagnostic for Estimating Key Beam Descriptors 969
 
  • K.R.L. Baker, I.D. Finch, S.R. Lawrie, A.A. Saoulis
    STFC/RAL/ISIS, Chilton, Didcot, Oxon, United Kingdom
  • S. Basak, J. Cha, J. Thiyagalingam
    STFC/RAL/SCD, Didcot, United Kingdom
 
  Funding: Science and Technology Facilities Council (STFC), U.K. Research and Innovation (UKRI)
Real-time beam descriptive data such as emittance, envelope and loss, are central to accelerator operations, including online diagnostics, maintenance and beam quality control. However, these cannot always be obtained without disrupting user runs. Physics-based simulations, such as particle tracking codes, can be leveraged to provide estimates of these beam descriptors. However, such simulation-based methods are computationally intensive requiring access to high performance computing facilities, and hence, they are often non-realistic for real-time purposes. The proposed work explores the feasibility of using machine learning to replace these simulations with fast-executing inference models based on surrogate modelling. The approach is intended to provide the operators with estimates of key beam properties in real time. Bayesian optimisation is used to generate a synthetic dataset to ensure the input space is efficiently sampled and representative of operating conditions. This is used to train a surrogate model to predict beam envelope, emittance and loss. The methodology is applied to the ISIS MEBT as a case study to evaluate the performance of the surrogate model.
 
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-TUPOST048  
About • Received ※ 01 June 2022 — Revised ※ 12 June 2022 — Accepted ※ 27 June 2022 — Issue date ※ 02 July 2022
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TUPOPT057 Using Surrogate Models to Assist Accelerator Tuning at ISIS 1133
 
  • A.A. Saoulis, K.R.L. Baker, H.V. Cavanagh, R.E. Williamson
    STFC/RAL/ISIS, Chilton, Didcot, Oxon, United Kingdom
  • S. Basak, J. Cha, J. Thiyagalingam
    STFC/RAL/SCD, Didcot, United Kingdom
 
  Funding: STFC and UKRI
High intensity hadron accelerator performance is often dominated by the need to minimise and control beam losses. Operator efforts to tune the machine during live operation are often restricted to local parameter space searches, while existing physics-based simulations are generally too computationally expensive to aid tuning in real-time. To this end, Machine Learning-based surrogate models can be trained on data produced by physics-based simulations, and serve to produce fast, accurate predictions of key beam properties, such as beam phase and bunch shape over time. These models can be used as a virtual diagnostic tool to explore the parameter space of the accelerator in real-time, without making changes on the live machine. At the ISIS Neutron and Muon source, major beam losses in the synchrotron are caused by injection and longitudinal trapping processes, as well as high intensity effects. This paper describes the training and inference performance of a neural network surrogate model of the longitudinal beam dynamics in the ISIS synchrotron, from injection at 70 MeV to 800 MeV extraction, and evaluates the model’s ability to assist accelerator tuning.
 
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-TUPOPT057  
About • Received ※ 07 June 2022 — Revised ※ 12 June 2022 — Accepted ※ 13 June 2022 — Issue date ※ 03 July 2022
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TUPOPT063 Vsystem to EPICS Control System Transition at the ISIS Accelerators 1156
 
  • I.D. Finch, B.R. Aljamal, K.R.L. Baker, R. Brodie, J.-L. Fernández-Hernando, G.D. Howells, M.F. Leputa, S.A. Medley, A.A. Saoulis
    STFC/RAL/ISIS, Chilton, Didcot, Oxon, United Kingdom
  • A. Kurup
    Imperial College of Science and Technology, Department of Physics, London, United Kingdom
 
  The ISIS Neutron and Muon Source at Rutherford Appleton Laboratory is a pulsed source used for research in material and life sciences. A linac and synchrotron accelerate protons to produce neutrons in two spallation targets. The accelerators are currently operated using commercial Vsystem control software. A transition to the EPICS control system is underway, with the end goal of a containerised system preferring the pvAccess protocol. We report the progress of this transition, which is being done without disrupting ISIS operations. We describe a bidirectional interface between Vsystem and EPICS that enables the two control systems to co-exist and interact. This allows us to decouple the transition of controls UI from the associated hardware. Automated conversion of the binary-format Vsystem control screens has been developed that replicates the current interface in EPICS, allowing minimal retraining of operators. We also outline the development of EPICS interfaces to standard and unique-to-ISIS hardware, reuse of and managing continuity of existing long-term data archiving, the development of EPICS interfaces to standard and unique-to-ISIS hardware, and migration of alerts.  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-TUPOPT063  
About • Received ※ 25 May 2022 — Accepted ※ 13 June 2022 — Issue date ※ 16 June 2022  
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