IBIC2022 - List of Authors (Edelen, J.P.)

Paper	Title	Page
MOP10	Removing Noise in BPM Measurements with Variational Autoencoders	43
	J.P. Edelen, J.A. Einstein-Curtis, C.C. Hall, M.J. Henderson RadiaSoft LLC, Boulder, Colorado, USA A.L. Romanov Fermilab, Batavia, Illinois, 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. Noise in beam measurements is an ever-present challenge in accelerator operations. In addition to the challenges presented by hardware and signal processing, new operational regimes, such as ultra-short bunches, create additional difficulties in routine beam measurements. Techniques in machine learning have been successfully applied in other domains to overcome challenges inherent in noisy data. Variational autoencoders (VAEs) are shown to be capable of removing significant leevels of noise. A VAE can be used as a pre-processing tool for noise removal before the de-noised data is analyzed via other methods, or the VAE can be directly used to make beam dynamics measurements. Here we present the use of VAEs as a tool for addressing noise in BPM measurements.
DOI •	reference for this paper ※ doi:10.18429/JACoW-IBIC2022-MOP10
About •	Received ※ 29 August 2022 — Revised ※ 10 September 2022 — Accepted ※ 11 September 2022 — Issue date ※ 24 November 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUP39	Neural Network Inverse Models for Implicit Optics Tuning in the AGS to RHIC Transfer Line	327
	J.P. Edelen, N.M. Cook, J.A. Einstein-Curtis RadiaSoft LLC, Boulder, Colorado, USA K.A. Brown, V. Schoefer BNL, Upton, New York, USA
	Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under Award Number DE-SC0019682 One of the fundamental challenges of using machine-learning-based inverse models for optics tuning in accelerators, particularly transfer lines, is the degenerate nature of the magnet settings and beam envelope functions. Moreover, it is challenging, if not impossible, to train a neural network to compute correct quadrupole settings from a given set of measurements due to the limited number of diagnostics available in operational beamlines. However, models that relate BPM readings to corrector settings are more forgiving, and have seen significant success as a benchmark for machine learning inverse models. We recently demonstrated that when comparing predicted corrector settings to actual corrector settings from a BPM inverse model, the model error can be related to errors in quadrupole settings. In this paper, we expand on that effort by incorporating inverse model errors as an optimization tool to correct for optics errors in a beamline. We present a toy model using a FODO lattice and then demonstrate the use of this technique for optics corrections in the AGS to RHIC transfer line at BNL.
DOI •	reference for this paper ※ doi:10.18429/JACoW-IBIC2022-TUP39
About •	Received ※ 05 September 2022 — Revised ※ 10 September 2022 — Accepted ※ 11 September 2022 — Issue date ※ 12 November 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Removing Noise in BPM Measurements with Variational Autoencoders

43

J.P. Edelen, J.A. Einstein-Curtis, C.C. Hall, M.J. Henderson
RadiaSoft LLC, Boulder, Colorado, USA
A.L. Romanov
Fermilab, Batavia, Illinois, 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.
Noise in beam measurements is an ever-present challenge in accelerator operations. In addition to the challenges presented by hardware and signal processing, new operational regimes, such as ultra-short bunches, create additional difficulties in routine beam measurements. Techniques in machine learning have been successfully applied in other domains to overcome challenges inherent in noisy data. Variational autoencoders (VAEs) are shown to be capable of removing significant leevels of noise. A VAE can be used as a pre-processing tool for noise removal before the de-noised data is analyzed via other methods, or the VAE can be directly used to make beam dynamics measurements. Here we present the use of VAEs as a tool for addressing noise in BPM measurements.

DOI •

reference for this paper ※ doi:10.18429/JACoW-IBIC2022-MOP10

About •

Received ※ 29 August 2022 — Revised ※ 10 September 2022 — Accepted ※ 11 September 2022 — Issue date ※ 24 November 2022

Cite •

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

TUP39

Neural Network Inverse Models for Implicit Optics Tuning in the AGS to RHIC Transfer Line

327

J.P. Edelen, N.M. Cook, J.A. Einstein-Curtis
RadiaSoft LLC, Boulder, Colorado, USA
K.A. Brown, V. Schoefer
BNL, Upton, New York, USA

Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under Award Number DE-SC0019682
One of the fundamental challenges of using machine-learning-based inverse models for optics tuning in accelerators, particularly transfer lines, is the degenerate nature of the magnet settings and beam envelope functions. Moreover, it is challenging, if not impossible, to train a neural network to compute correct quadrupole settings from a given set of measurements due to the limited number of diagnostics available in operational beamlines. However, models that relate BPM readings to corrector settings are more forgiving, and have seen significant success as a benchmark for machine learning inverse models. We recently demonstrated that when comparing predicted corrector settings to actual corrector settings from a BPM inverse model, the model error can be related to errors in quadrupole settings. In this paper, we expand on that effort by incorporating inverse model errors as an optimization tool to correct for optics errors in a beamline. We present a toy model using a FODO lattice and then demonstrate the use of this technique for optics corrections in the AGS to RHIC transfer line at BNL.

DOI •

reference for this paper ※ doi:10.18429/JACoW-IBIC2022-TUP39

About •

Received ※ 05 September 2022 — Revised ※ 10 September 2022 — Accepted ※ 11 September 2022 — Issue date ※ 12 November 2022

Cite •

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