NAPAC2022 - List of Authors (Lobach, I.)

Paper	Title	Page
TUYE4	Machine Learning for Anomaly Detection and Classification in Particle Accelerators	311
	I. Lobach, M. Borland, K.C. Harkay, N. Kuklev, A. Sannibale, Y. Sun ANL, Lemont, Illinois, USA
	Funding: The work is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357. We explore the possibility of using a Machine Learning (ML) algorithm to identify the source of occasional poor performance of the Particle Accumulator Ring (PAR) and the Linac-To-PAR (LTP) transport line, which are parts of the injector complex of the Advanced Photon Source (APS) at Argonne National Lab. The cause of reduced injection or extraction efficiencies may be as simple as one parameter being out of range. Still, it may take an expert considerable time to notice it, whereas a well-trained ML model can point at it instantly. In addition, a machine expert might not be immediately available when a problem occurs. Therefore, we began by focusing on such single-parameter anomalies. The training data were generated by creating controlled perturbations of several parameters of PAR and LTP one-by-one, while continuously logging all available process variables. Then, several ML classifiers were trained to recognize certain signatures in the logged data and link them to the sources of poor machine performance. Possible applications of autoencoders and variational autoencoders for unsupervised anomaly detection and for anomaly clustering were considered as well.
	Slides TUYE4 [9.534 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUYE4
About •	Received ※ 03 August 2022 — Revised ※ 07 August 2022 — Accepted ※ 08 August 2022 — Issue date ※ 28 August 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPA29	Machine Learning for Predicting Power Supply Trips in Storage Rings	413
	I. Lobach, M. Borland, G.I. Fystro, A. Sannibale, Y. Sun ANL, Lemont, Illinois, USA A. Diaw, J.P. Edelen RadiaSoft LLC, Boulder, Colorado, USA
	Funding: The work is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357. In the Advanced Photon Source (APS) storage ring at Argonne National Lab, trips in the magnet power supplies (PSs) lead to a complete electron beam loss a few times a year. This results in unexpected interruptions of the users’ experiments. In this contribution, we investigate the historical data for the last two decades to find precursors for the PS trips that could provide an advance notice for future trips and allow some preventive action by the ring operator or by the PS maintenance team. Various unsupervised anomaly detection models can be trained on the vast amounts of available reference data from the beamtime periods that ended with an intentional beam dump. We find that such models can sometimes detect trip precursors in PS currents, voltages, and in the temperatures of magnets, capacitors and transistors (components of PSs).
	Poster TUPA29 [2.116 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA29
About •	Received ※ 03 August 2022 — Revised ※ 07 August 2022 — Accepted ※ 10 August 2022 — Issue date ※ 18 August 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

FRXD1	Demonstration of Optical Stochastic Cooling in an Electron Storage Ring
	J.D. Jarvis, D.R. Broemmelsiek, K. Carlson, D.R. Edstrom, V.A. Lebedev, S. Nagaitsev, H. Piekarz, A.L. Romanov, J. Ruan, J.K. Santucci, G. Stancari, A. Valishev Fermilab, Batavia, Illinois, USA S. Chattopadhyay, A.J. Dick, P. Piot Northern Illinois University, DeKalb, Illinois, USA I. Lobach University of Chicago, Chicago, Illinois, USA
	Optical stochastic cooling (OSC), proposed nearly thirty years ago, replaces the conventional microwave elements of stochastic cooling (SC) with optical-frequency analogs, such as undulators, optical lenses and optical amplifiers. Here we discuss the first experimental observation of OSC, which was performed at the Fermi National Accelerator Laboratory’s Integrable Optics Test Accelerator (IOTA) with 100-MeV electrons and a radiation wavelength of 950 nm. The experiment employed a non-amplified configuration of OSC and achieved a longitudinal damping rate close to one order of magnitude larger than the beam’s natural damping due to synchrotron radiation. The integrated system demonstrated sub-femtosecond stability and a bandwidth of ~20 THz, a factor of ~2000-times higher than conventional microwave SC systems. Coupling to the transverse planes enabled simultaneous cooling of the beam in all degrees of freedom. This first demonstration of SC at optical frequencies serves as a foundation for more advanced experiments with high-gain optical amplification and advances opportunities for future operational OSC systems at colliders and other accelerator facilities.
	Slides FRXD1 [32.041 MB]
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Paper

Title

Page

Machine Learning for Anomaly Detection and Classification in Particle Accelerators

311

I. Lobach, M. Borland, K.C. Harkay, N. Kuklev, A. Sannibale, Y. Sun
ANL, Lemont, Illinois, USA

Funding: The work is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357.
We explore the possibility of using a Machine Learning (ML) algorithm to identify the source of occasional poor performance of the Particle Accumulator Ring (PAR) and the Linac-To-PAR (LTP) transport line, which are parts of the injector complex of the Advanced Photon Source (APS) at Argonne National Lab. The cause of reduced injection or extraction efficiencies may be as simple as one parameter being out of range. Still, it may take an expert considerable time to notice it, whereas a well-trained ML model can point at it instantly. In addition, a machine expert might not be immediately available when a problem occurs. Therefore, we began by focusing on such single-parameter anomalies. The training data were generated by creating controlled perturbations of several parameters of PAR and LTP one-by-one, while continuously logging all available process variables. Then, several ML classifiers were trained to recognize certain signatures in the logged data and link them to the sources of poor machine performance. Possible applications of autoencoders and variational autoencoders for unsupervised anomaly detection and for anomaly clustering were considered as well.

Slides TUYE4 [9.534 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUYE4

About •

Received ※ 03 August 2022 — Revised ※ 07 August 2022 — Accepted ※ 08 August 2022 — Issue date ※ 28 August 2022

Cite •

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

TUPA29

Machine Learning for Predicting Power Supply Trips in Storage Rings

413

I. Lobach, M. Borland, G.I. Fystro, A. Sannibale, Y. Sun
ANL, Lemont, Illinois, USA
A. Diaw, J.P. Edelen
RadiaSoft LLC, Boulder, Colorado, USA

Funding: The work is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357.
In the Advanced Photon Source (APS) storage ring at Argonne National Lab, trips in the magnet power supplies (PSs) lead to a complete electron beam loss a few times a year. This results in unexpected interruptions of the users’ experiments. In this contribution, we investigate the historical data for the last two decades to find precursors for the PS trips that could provide an advance notice for future trips and allow some preventive action by the ring operator or by the PS maintenance team. Various unsupervised anomaly detection models can be trained on the vast amounts of available reference data from the beamtime periods that ended with an intentional beam dump. We find that such models can sometimes detect trip precursors in PS currents, voltages, and in the temperatures of magnets, capacitors and transistors (components of PSs).

Poster TUPA29 [2.116 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA29

About •

Received ※ 03 August 2022 — Revised ※ 07 August 2022 — Accepted ※ 10 August 2022 — Issue date ※ 18 August 2022

Cite •

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

FRXD1

Demonstration of Optical Stochastic Cooling in an Electron Storage Ring

J.D. Jarvis, D.R. Broemmelsiek, K. Carlson, D.R. Edstrom, V.A. Lebedev, S. Nagaitsev, H. Piekarz, A.L. Romanov, J. Ruan, J.K. Santucci, G. Stancari, A. Valishev
Fermilab, Batavia, Illinois, USA
S. Chattopadhyay, A.J. Dick, P. Piot
Northern Illinois University, DeKalb, Illinois, USA
I. Lobach
University of Chicago, Chicago, Illinois, USA

Optical stochastic cooling (OSC), proposed nearly thirty years ago, replaces the conventional microwave elements of stochastic cooling (SC) with optical-frequency analogs, such as undulators, optical lenses and optical amplifiers. Here we discuss the first experimental observation of OSC, which was performed at the Fermi National Accelerator Laboratory’s Integrable Optics Test Accelerator (IOTA) with 100-MeV electrons and a radiation wavelength of 950 nm. The experiment employed a non-amplified configuration of OSC and achieved a longitudinal damping rate close to one order of magnitude larger than the beam’s natural damping due to synchrotron radiation. The integrated system demonstrated sub-femtosecond stability and a bandwidth of ~20 THz, a factor of ~2000-times higher than conventional microwave SC systems. Coupling to the transverse planes enabled simultaneous cooling of the beam in all degrees of freedom. This first demonstration of SC at optical frequencies serves as a foundation for more advanced experiments with high-gain optical amplification and advances opportunities for future operational OSC systems at colliders and other accelerator facilities.

Slides FRXD1 [32.041 MB]

Cite •

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