NAPAC2022 - List of Authors (Cook, N.M.)

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)

TUPA21	Hydrodynamic and Beam Dynamic Simulations of Ultra-Low Emittance Whole Beam Dumps in the Advanced Photon Source Storage Ring	390
	J.C. Dooling, M. Borland, A.M. Grannan, C.J. Graziani, Y. Lee, R.R. Lindberg, G. Navrotski ANL, Lemont, Illinois, USA N.M. Cook RadiaSoft LLC, Boulder, Colorado, USA D.W. Lee UCSC, Santa Cruz, California, USA
	Funding: Work supported by Accelerator Science and Technology LDRD Project 2021-0119 and the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357. The Advanced Photon Source Upgrade will use a multi-bend achromatic lattice to reduce vertical and horizontal beam emittances by one- and two-orders of magnitude respectively; in addition operating current will double. The resulting electron beam will be capable of depositing more than 150 MGy on machine protection collimators creating high-energy-density conditions. Work is underway to couple the beam dynamics code Elegant with the particle-matter interaction program MARS and the magnetohydrodynamics code FLASH to model the effects of whole beam dumps on the collimators. Loss distributions from Elegant are input to MARS which provide dose maps to FLASH. We also examine the propagation of downstream shower components after the beam interacts with the collimator. Electrons and positrons are tracked to determine locations of beam loss. Beam dump experiments conducted in the APS storage-ring, generated dose levels as high as 30 MGy resulting in severe damage to the collimator surfaces with melting in the bulk. The deformed collimator surface may lead to beam deposition in unexpected locations. A fan-out kicker is planned to mitigate the effects of whole beam dumps on the collimators.
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA21
About •	Received ※ 02 August 2022 — Revised ※ 10 August 2022 — Accepted ※ 11 August 2022 — Issue date ※ 10 September 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

THZE4	Experimental Characterization of Gas Sheet Transverse Profile Diagnostic	907
	N. Burger, G. Andonian, D.I. Gavryushkin, T.J. Hodgetts, A.-L.M.S. Lamure, M. Ruelas RadiaBeam, Santa Monica, California, USA N.M. Cook, A. Diaw RadiaSoft LLC, Boulder, Colorado, USA P.E. Denham, P. Musumeci, A. Ody UCLA, Los Angeles, USA N.P. Norvell UCSC, Santa Cruz, California, USA C.P. Welsch, M. Yadav The University of Liverpool, Liverpool, United Kingdom C.P. Welsch Cockcroft Institute, Warrington, Cheshire, United Kingdom
	Transverse profile diagnostics for high-intensity beams require solutions that are non-intercepting and single-shot. In this paper, we describe a gas-sheet ionization diagnostic that employs a precision-shaped, neutral gas jet. As the high-intensity beam passes through the gas sheet, neutral particles are ionized. The ionization products are transported and imaged on a detector. A neural-network based reconstruction algorithm, trained on simulation data, then outputs the initial transverse conditions of the beam prior to ionization. The diagnostic is also adaptable to image the photons from recombination. Preliminary tests at low energy are presented to characterize the working principle of the instrument, including comparisons to existing diagnostics. The results are parametrized as a function of beam charge, spot size, and bunch length.
	Slides THZE4 [2.051 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-THZE4
About •	Received ※ 02 August 2022 — Revised ※ 09 August 2022 — Accepted ※ 10 August 2022 — Issue date ※ 09 October 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)

TUPA21

Hydrodynamic and Beam Dynamic Simulations of Ultra-Low Emittance Whole Beam Dumps in the Advanced Photon Source Storage Ring

390

J.C. Dooling, M. Borland, A.M. Grannan, C.J. Graziani, Y. Lee, R.R. Lindberg, G. Navrotski
ANL, Lemont, Illinois, USA
N.M. Cook
RadiaSoft LLC, Boulder, Colorado, USA
D.W. Lee
UCSC, Santa Cruz, California, USA

Funding: Work supported by Accelerator Science and Technology LDRD Project 2021-0119 and the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357.
The Advanced Photon Source Upgrade will use a multi-bend achromatic lattice to reduce vertical and horizontal beam emittances by one- and two-orders of magnitude respectively; in addition operating current will double. The resulting electron beam will be capable of depositing more than 150 MGy on machine protection collimators creating high-energy-density conditions. Work is underway to couple the beam dynamics code Elegant with the particle-matter interaction program MARS and the magnetohydrodynamics code FLASH to model the effects of whole beam dumps on the collimators. Loss distributions from Elegant are input to MARS which provide dose maps to FLASH. We also examine the propagation of downstream shower components after the beam interacts with the collimator. Electrons and positrons are tracked to determine locations of beam loss. Beam dump experiments conducted in the APS storage-ring, generated dose levels as high as 30 MGy resulting in severe damage to the collimator surfaces with melting in the bulk. The deformed collimator surface may lead to beam deposition in unexpected locations. A fan-out kicker is planned to mitigate the effects of whole beam dumps on the collimators.

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA21

About •

Received ※ 02 August 2022 — Revised ※ 10 August 2022 — Accepted ※ 11 August 2022 — Issue date ※ 10 September 2022

Cite •

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

THZE4

Experimental Characterization of Gas Sheet Transverse Profile Diagnostic

907

N. Burger, G. Andonian, D.I. Gavryushkin, T.J. Hodgetts, A.-L.M.S. Lamure, M. Ruelas
RadiaBeam, Santa Monica, California, USA
N.M. Cook, A. Diaw
RadiaSoft LLC, Boulder, Colorado, USA
P.E. Denham, P. Musumeci, A. Ody
UCLA, Los Angeles, USA
N.P. Norvell
UCSC, Santa Cruz, California, USA
C.P. Welsch, M. Yadav
The University of Liverpool, Liverpool, United Kingdom
C.P. Welsch
Cockcroft Institute, Warrington, Cheshire, United Kingdom

Transverse profile diagnostics for high-intensity beams require solutions that are non-intercepting and single-shot. In this paper, we describe a gas-sheet ionization diagnostic that employs a precision-shaped, neutral gas jet. As the high-intensity beam passes through the gas sheet, neutral particles are ionized. The ionization products are transported and imaged on a detector. A neural-network based reconstruction algorithm, trained on simulation data, then outputs the initial transverse conditions of the beam prior to ionization. The diagnostic is also adaptable to image the photons from recombination. Preliminary tests at low energy are presented to characterize the working principle of the instrument, including comparisons to existing diagnostics. The results are parametrized as a function of beam charge, spot size, and bunch length.

Slides THZE4 [2.051 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-THZE4

About •

Received ※ 02 August 2022 — Revised ※ 09 August 2022 — Accepted ※ 10 August 2022 — Issue date ※ 09 October 2022

Cite •

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