NAPAC2022 - List of Authors (Welsch, C.P.)

Paper	Title	Page
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)

TUPA84	Reconstructing Beam Parameters from Betatron Radiation Through Machine Learning and Maximum Likelihood Estimation	527
	S. Zhang, N. Majernik, B. Naranjo, J.B. Rosenzweig, M. Yadav UCLA, Los Angeles, California, USA Ö. Apsimon, C.P. Welsch, M. Yadav The University of Liverpool, Liverpool, United Kingdom
	Funding: US Department of Energy, Division of High Energy Physics, under Contract No. DE-SC0009914. The dense drive beam used in plasma wakefield acceleration generates a linear focusing force that causes electrons inside the witness beam to undergo betatron oscillations, giving rise to betatron radiation. Because information about the properties of the beam is encoded in the betatron radiation, measurements of the radiation such as those recorded by the UCLA-built Compton spectrometer can be used to reconstruct beam parameters. Two possible methods of extracting information about beam parameters from measurements of radiation are machine learning (ML), which is increasingly being implemented for different fields of beam diagnostics, and a statistical technique known as maximum likelihood estimation (MLE). We assess the ability of both machine learning and MLE methods to accurately extract beam parameters from measurements of betatron radiation.
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA84
About •	Received ※ 02 August 2022 — Revised ※ 07 August 2022 — Accepted ※ 10 August 2022 — Issue date ※ 05 October 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

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)

TUPA84

Reconstructing Beam Parameters from Betatron Radiation Through Machine Learning and Maximum Likelihood Estimation

527

S. Zhang, N. Majernik, B. Naranjo, J.B. Rosenzweig, M. Yadav
UCLA, Los Angeles, California, USA
Ö. Apsimon, C.P. Welsch, M. Yadav
The University of Liverpool, Liverpool, United Kingdom

Funding: US Department of Energy, Division of High Energy Physics, under Contract No. DE-SC0009914.
The dense drive beam used in plasma wakefield acceleration generates a linear focusing force that causes electrons inside the witness beam to undergo betatron oscillations, giving rise to betatron radiation. Because information about the properties of the beam is encoded in the betatron radiation, measurements of the radiation such as those recorded by the UCLA-built Compton spectrometer can be used to reconstruct beam parameters. Two possible methods of extracting information about beam parameters from measurements of radiation are machine learning (ML), which is increasingly being implemented for different fields of beam diagnostics, and a statistical technique known as maximum likelihood estimation (MLE). We assess the ability of both machine learning and MLE methods to accurately extract beam parameters from measurements of betatron radiation.

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA84

About •

Received ※ 02 August 2022 — Revised ※ 07 August 2022 — Accepted ※ 10 August 2022 — Issue date ※ 05 October 2022

Cite •

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