IPAC2022 - List of Authors (Lawrie, S.R.)

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
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

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

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)