NAPAC2022 - List of Authors (Hall, C.C.)

Paper	Title	Page
MOPA50	Integrated Photonics Structure Cathodes for Longitudinally Shaped Bunch Trains	160
	S.J. Coleman, D.T. Abell, C.C. Hall RadiaSoft LLC, Boulder, Colorado, USA R. Kapadia University of Southern California, Los Angeles, California, USA S.S. Karkare Arizona State University, Tempe, USA S.Y. Kim, P. Piot, J.F. Power ANL, Lemont, 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 DOE DE-SC0021681 Compact, high-gradient structure wakefield accelerators can operate at improved efficiency using shaped electron beams, such as a high transformer ratio beam shape, to drive the wakes. These shapes have generally come from a photocathode gun followed by a transverse mask to imprint a desired shape on the transverse distribution, and then an emittance exchanger (EEX) to convert that transverse shape into a longitudinal distribution. This process discards some large fraction of the beam, limiting wall-plug efficiency as well as leaving a solid object in the path of the beam. In this paper, we present a proposed method of using integrated photonics structures to control the emission pattern on the cathode surface. This transverse pattern is then converted into a longitudinal pattern at the end of an EEX. This removes the need for the mask, preserving the total charge produced at the cathode surface. We present simulations of an experimental set-up to demonstrate this concept at the Argonne Wakefield Accelerator.
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA50
About •	Received ※ 03 August 2022 — Revised ※ 05 August 2022 — Accepted ※ 26 August 2022 — Issue date ※ 03 October 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

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)

TUPA36	The Advanced Photon Source Linac Extension Area Beamline	430
	K.P. Wootton, W. Berg, J.M. Byrd, J.C. Dooling, G.I. Fystro, A.H. Lumpkin, Y. Sun, A. Zholents ANL, Lemont, Illinois, USA C.C. Hall RadiaSoft LLC, Boulder, Colorado, USA
	Funding: This research used resources of the Advanced Photon Source, operated for the U.S. Department of Energy Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357. The Linac Extension Area at the Advanced Photon Source is a flexible beamline area for testing accelerator components and techniques. Driven by the Advanced Photon Source electron linac equipped with a photocathode RF electron gun, the Linac Extension Area houses a 12 m long beamline. The beamline is furnished with YAG screens, BPMs and a magnetic spectrometer to assist with characterization of beam emittance and energy spread. A 1.4 m long insertion in the middle of the beamline is provided for the installation of a device under test. The beamline is expected to be available soon for testing accelerator components and techniques using round and flat electron beams over an energy range 150-450 MeV. In the present work, we describe this beamline and summarise the main beam parameters.
	Poster TUPA36 [0.892 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA36
About •	Received ※ 02 August 2022 — Revised ※ 08 August 2022 — Accepted ※ 10 August 2022 — Issue date ※ 19 September 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Integrated Photonics Structure Cathodes for Longitudinally Shaped Bunch Trains

160

S.J. Coleman, D.T. Abell, C.C. Hall
RadiaSoft LLC, Boulder, Colorado, USA
R. Kapadia
University of Southern California, Los Angeles, California, USA
S.S. Karkare
Arizona State University, Tempe, USA
S.Y. Kim, P. Piot, J.F. Power
ANL, Lemont, 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 DOE DE-SC0021681
Compact, high-gradient structure wakefield accelerators can operate at improved efficiency using shaped electron beams, such as a high transformer ratio beam shape, to drive the wakes. These shapes have generally come from a photocathode gun followed by a transverse mask to imprint a desired shape on the transverse distribution, and then an emittance exchanger (EEX) to convert that transverse shape into a longitudinal distribution. This process discards some large fraction of the beam, limiting wall-plug efficiency as well as leaving a solid object in the path of the beam. In this paper, we present a proposed method of using integrated photonics structures to control the emission pattern on the cathode surface. This transverse pattern is then converted into a longitudinal pattern at the end of an EEX. This removes the need for the mask, preserving the total charge produced at the cathode surface. We present simulations of an experimental set-up to demonstrate this concept at the Argonne Wakefield Accelerator.

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA50

About •

Received ※ 03 August 2022 — Revised ※ 05 August 2022 — Accepted ※ 26 August 2022 — Issue date ※ 03 October 2022

Cite •

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

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)

TUPA36

The Advanced Photon Source Linac Extension Area Beamline

430

K.P. Wootton, W. Berg, J.M. Byrd, J.C. Dooling, G.I. Fystro, A.H. Lumpkin, Y. Sun, A. Zholents
ANL, Lemont, Illinois, USA
C.C. Hall
RadiaSoft LLC, Boulder, Colorado, USA

Funding: This research used resources of the Advanced Photon Source, operated for the U.S. Department of Energy Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357.
The Linac Extension Area at the Advanced Photon Source is a flexible beamline area for testing accelerator components and techniques. Driven by the Advanced Photon Source electron linac equipped with a photocathode RF electron gun, the Linac Extension Area houses a 12 m long beamline. The beamline is furnished with YAG screens, BPMs and a magnetic spectrometer to assist with characterization of beam emittance and energy spread. A 1.4 m long insertion in the middle of the beamline is provided for the installation of a device under test. The beamline is expected to be available soon for testing accelerator components and techniques using round and flat electron beams over an energy range 150-450 MeV. In the present work, we describe this beamline and summarise the main beam parameters.

Poster TUPA36 [0.892 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA36

About •

Received ※ 02 August 2022 — Revised ※ 08 August 2022 — Accepted ※ 10 August 2022 — Issue date ※ 19 September 2022

Cite •

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