NAPAC2022 - List of Authors (Nash, B.)

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)

MOPA57	Online Models for X-Ray Beamlines	170
	B. Nash, D.T. Abell, M.V. Keilman, P. Moeller, I.V. Pogorelov RadiaSoft LLC, Boulder, Colorado, USA Y. Du, A. Giles, J. Lynch, T. Morris, M.S. Rakitin, A. Walter BNL, Upton, New York, USA N.B. Goldring STATE33 Inc., Portland, Oregon, USA
	Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Science, under Award Number DE-SC0020593 X-ray beamlines transport synchrotron radiation from the magnetic source to the sample at a synchrotron light source. Alignment of elements such as mirrors and gratings are often done manually and can be quite time consuming. The use of photon beam models during operations is not common in the same way that they are used to great benefit for particle beams in accelerators. Linear and non-linear optics including the effects of coherence may be computed from source properties and augmented with measurements. In collaboration with NSLS-II, we are developing software tools and methods to include the model of the x-ray beam as it passes on its way to the sample. We are integrating the Blue-Sky beamline control toolkit with the Sirepo interface to several x-ray optics codes. Further, we are developing a simplified linear optics approach based on a Gauss-Schell model and linear canonical transforms as well as developing Machine Learning models for use directly from diagnostics data. We present progress on applying these ideas on NSLS-II beamlines and give a future outlook on this rather large and open domain for technological development.
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA57
About •	Received ※ 27 July 2022 — Revised ※ 02 August 2022 — Accepted ※ 07 August 2022 — Issue date ※ 11 August 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)

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)

MOPA57

Online Models for X-Ray Beamlines

170

B. Nash, D.T. Abell, M.V. Keilman, P. Moeller, I.V. Pogorelov
RadiaSoft LLC, Boulder, Colorado, USA
Y. Du, A. Giles, J. Lynch, T. Morris, M.S. Rakitin, A. Walter
BNL, Upton, New York, USA
N.B. Goldring
STATE33 Inc., Portland, Oregon, USA

Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Science, under Award Number DE-SC0020593
X-ray beamlines transport synchrotron radiation from the magnetic source to the sample at a synchrotron light source. Alignment of elements such as mirrors and gratings are often done manually and can be quite time consuming. The use of photon beam models during operations is not common in the same way that they are used to great benefit for particle beams in accelerators. Linear and non-linear optics including the effects of coherence may be computed from source properties and augmented with measurements. In collaboration with NSLS-II, we are developing software tools and methods to include the model of the x-ray beam as it passes on its way to the sample. We are integrating the Blue-Sky beamline control toolkit with the Sirepo interface to several x-ray optics codes. Further, we are developing a simplified linear optics approach based on a Gauss-Schell model and linear canonical transforms as well as developing Machine Learning models for use directly from diagnostics data. We present progress on applying these ideas on NSLS-II beamlines and give a future outlook on this rather large and open domain for technological development.

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA57

About •

Received ※ 27 July 2022 — Revised ※ 02 August 2022 — Accepted ※ 07 August 2022 — Issue date ※ 11 August 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)