IPAC2022 - List of Authors (Kilpatrick, M.C.)

Paper	Title	Page
MOPOTK038	BPM Analysis with Variational Autoencoders	543
	C.C. Hall, J.P. Edelen, J.A. Einstein-Curtis, M.C. Kilpatrick RadiaSoft LLC, Boulder, Colorado, USA
	Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of High Energy Physics under Award Number DE-SC0021699. In particle accelerators, beam position monitors (BPMs) are used extensively as a non-intercepting diagnostic. Significant information about beam dynamics can often be extracted from BPM measurements and used to actively tune the accelerator. However, common measurement tools, such as measurements of kicked beams, may become more difficult when very strong nonlinearities are present or when data is very noisy. In this work, we examine the use of variational autoencoders (VAEs) as a technique to extract measurements of the beam from simulated turn-by-turn BPM data. In particular, we show that VAEs may have the possibility to outperform other dimensionality reduction techniques that have historically been used to analyze such data. When the data collection period is limited, or the data is noisy, VAEs may offer significant advantages.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-MOPOTK038
About •	Received ※ 09 June 2022 — Revised ※ 13 June 2022 — Accepted ※ 15 June 2022 — Issue date ※ 10 July 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

BPM Analysis with Variational Autoencoders

543

C.C. Hall, J.P. Edelen, J.A. Einstein-Curtis, M.C. Kilpatrick
RadiaSoft LLC, Boulder, Colorado, USA

Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of High Energy Physics under Award Number DE-SC0021699.
In particle accelerators, beam position monitors (BPMs) are used extensively as a non-intercepting diagnostic. Significant information about beam dynamics can often be extracted from BPM measurements and used to actively tune the accelerator. However, common measurement tools, such as measurements of kicked beams, may become more difficult when very strong nonlinearities are present or when data is very noisy. In this work, we examine the use of variational autoencoders (VAEs) as a technique to extract measurements of the beam from simulated turn-by-turn BPM data. In particular, we show that VAEs may have the possibility to outperform other dimensionality reduction techniques that have historically been used to analyze such data. When the data collection period is limited, or the data is noisy, VAEs may offer significant advantages.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-MOPOTK038

About •

Received ※ 09 June 2022 — Revised ※ 13 June 2022 — Accepted ※ 15 June 2022 — Issue date ※ 10 July 2022

Cite •

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