NAPAC2022 - List of Authors (Edelen, J.P.)

Paper	Title	Page
MOPA55	Facilitating Machine Learning Collaborations Between Labs, Universities, and Industry	164
	J.P. Edelen, D.T. Abell, D.L. Bruhwiler, S.J. Coleman, N.M. Cook, A. Diaw, J.A. Einstein-Curtis, C.C. Hall, M.C. Kilpatrick, B. Nash, I.V. Pogorelov RadiaSoft LLC, Boulder, Colorado, USA K.A. Brown BNL, Upton, New York, USA S. Calder ORNL RAD, Oak Ridge, Tennessee, USA A.L. Edelen, B.D. O’Shea, R.J. Roussel SLAC, Menlo Park, California, USA C.M. Hoffmann ORNL, Oak Ridge, Tennessee, USA E.-C. Huang LANL, Los Alamos, New Mexico, USA P. Piot Northern Illinois University, DeKalb, Illinois, USA C. Tennant JLab, Newport News, Virginia, USA
	It is clear from numerous recent community reports, papers, and proposals that machine learning is of tremendous interest for particle accelerator applications. The quickly evolving landscape continues to grow in both the breadth and depth of applications including physics modeling, anomaly detection, controls, diagnostics, and analysis. Consequently, laboratories, universities, and companies across the globe have established dedicated machine learning (ML) and data-science efforts aiming to make use of these new state-of-the-art tools. The current funding environment in the U.S. is structured in a way that supports specific application spaces rather than larger collaboration on community software. Here, we discuss the existing collaboration bottlenecks and how a shift in the funding environment, and how we develop collaborative tools, can help fuel the next wave of ML advancements for particle accelerators.
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA55
About •	Received ※ 10 August 2022 — Revised ※ 11 August 2022 — Accepted ※ 22 August 2022 — Issue date ※ 01 September 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUZG1	Magnets to ML to Light Sources: Designing from the Browser with Sirepo
	J.P. Edelen, B. Nash RadiaSoft LLC, Boulder, Colorado, USA R.J. Roussel SLAC, Menlo Park, California, USA
	Join us for an afternoon intensive with the Sirepo CAE & Design platform! Experts will present their work and lead tutorials using Sirepo’s apps and tools for magnet design, ML techniques in control systems, and X-ray beamline modeling. Bring your own laptop and pre-register to use the free gateway at Sirepo.com for practical exercises. Dr. Edelen will provide a tutorial on how to design dipoles and undulators using parameterized magnets. Dr. Roussel will demonstrate combining the classical Preisach model of hysteresis with ML techniques to efficiently create non-parametric, high-fidelity models of arbitrary systems exhibiting hysteresis. Also shown will be how using these joint hysteresis-beam models allows users to overcome optimization performance limitations when hysteresis effects are ignored. Dr. Nash will review the capabilities of Shadow and SRW within Sirepo, discuss additional optics tools in the interface including brightness and linear optics, demonstrate translation between the two codes, and show how to continue your simulation work in a python based Jupyter notebook.
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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)

Paper

Title

Page

Facilitating Machine Learning Collaborations Between Labs, Universities, and Industry

164

J.P. Edelen, D.T. Abell, D.L. Bruhwiler, S.J. Coleman, N.M. Cook, A. Diaw, J.A. Einstein-Curtis, C.C. Hall, M.C. Kilpatrick, B. Nash, I.V. Pogorelov
RadiaSoft LLC, Boulder, Colorado, USA
K.A. Brown
BNL, Upton, New York, USA
S. Calder
ORNL RAD, Oak Ridge, Tennessee, USA
A.L. Edelen, B.D. O’Shea, R.J. Roussel
SLAC, Menlo Park, California, USA
C.M. Hoffmann
ORNL, Oak Ridge, Tennessee, USA
E.-C. Huang
LANL, Los Alamos, New Mexico, USA
P. Piot
Northern Illinois University, DeKalb, Illinois, USA
C. Tennant
JLab, Newport News, Virginia, USA

It is clear from numerous recent community reports, papers, and proposals that machine learning is of tremendous interest for particle accelerator applications. The quickly evolving landscape continues to grow in both the breadth and depth of applications including physics modeling, anomaly detection, controls, diagnostics, and analysis. Consequently, laboratories, universities, and companies across the globe have established dedicated machine learning (ML) and data-science efforts aiming to make use of these new state-of-the-art tools. The current funding environment in the U.S. is structured in a way that supports specific application spaces rather than larger collaboration on community software. Here, we discuss the existing collaboration bottlenecks and how a shift in the funding environment, and how we develop collaborative tools, can help fuel the next wave of ML advancements for particle accelerators.

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA55

About •

Received ※ 10 August 2022 — Revised ※ 11 August 2022 — Accepted ※ 22 August 2022 — Issue date ※ 01 September 2022

Cite •

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

TUZG1

Magnets to ML to Light Sources: Designing from the Browser with Sirepo

J.P. Edelen, B. Nash
RadiaSoft LLC, Boulder, Colorado, USA
R.J. Roussel
SLAC, Menlo Park, California, USA

Join us for an afternoon intensive with the Sirepo CAE & Design platform! Experts will present their work and lead tutorials using Sirepo’s apps and tools for magnet design, ML techniques in control systems, and X-ray beamline modeling. Bring your own laptop and pre-register to use the free gateway at Sirepo.com for practical exercises. Dr. Edelen will provide a tutorial on how to design dipoles and undulators using parameterized magnets. Dr. Roussel will demonstrate combining the classical Preisach model of hysteresis with ML techniques to efficiently create non-parametric, high-fidelity models of arbitrary systems exhibiting hysteresis. Also shown will be how using these joint hysteresis-beam models allows users to overcome optimization performance limitations when hysteresis effects are ignored. Dr. Nash will review the capabilities of Shadow and SRW within Sirepo, discuss additional optics tools in the interface including brightness and linear optics, demonstrate translation between the two codes, and show how to continue your simulation work in a python based Jupyter notebook.

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)