NAPAC2022 - List of Authors (Huang, E.-C.)

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)

WEPA42	A Modular X-Ray Detector for Beamline Diagnostics at LANL	725
	P.M. Freeman, B. Odegard, R. Schmitz, D. Stuart, J. Yang UCSB, Santa Barbara, California, USA J. Bohon, M.S. Gulley, E.-C. Huang, J. Smedley LANL, Los Alamos, New Mexico, USA L. Malavasi WPI, Worcester, MA, USA
	An X-ray detector is being developed for diagnostic measurement and monitoring of the Drift Tube LINAC (DTL) at the Los Alamos Neutron Science Center (LANSCE) at Los Alamos National Lab. The detector will consist of a row of x-ray spectrometers adjacent to the DTL that will measure the spectrum of X-rays resulting from bremsstrahlung of electrons created in vacuum by the RF. Each spectrometer will monitor a specific gap between drift tubes, and will consist of an array of scintillating crystals coupled to SiPMs read out with custom-built electronics. The spectrometer is designed with one LYSO and three NaI crystals. The LYSO provides a tagged gamma source with three peaks that are used for calibration of the NaI. A prototype of the spectrometer was tested at the LANSCE DTL to validate the feasibility of measuring gamma spectra and performing self-calibration in situ. A summary of test results with the LANSCE prototype will be presented, along with a detector system design that aims to be modular and inexpensive across all modules in the DTL. Plans for future development will be presented as well.
	Poster WEPA42 [1.308 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-WEPA42
About •	Received ※ 04 August 2022 — Revised ※ 06 August 2022 — Accepted ※ 09 August 2022 — Issue date ※ 11 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)

WEPA42

A Modular X-Ray Detector for Beamline Diagnostics at LANL

725

P.M. Freeman, B. Odegard, R. Schmitz, D. Stuart, J. Yang
UCSB, Santa Barbara, California, USA
J. Bohon, M.S. Gulley, E.-C. Huang, J. Smedley
LANL, Los Alamos, New Mexico, USA
L. Malavasi
WPI, Worcester, MA, USA

An X-ray detector is being developed for diagnostic measurement and monitoring of the Drift Tube LINAC (DTL) at the Los Alamos Neutron Science Center (LANSCE) at Los Alamos National Lab. The detector will consist of a row of x-ray spectrometers adjacent to the DTL that will measure the spectrum of X-rays resulting from bremsstrahlung of electrons created in vacuum by the RF. Each spectrometer will monitor a specific gap between drift tubes, and will consist of an array of scintillating crystals coupled to SiPMs read out with custom-built electronics. The spectrometer is designed with one LYSO and three NaI crystals. The LYSO provides a tagged gamma source with three peaks that are used for calibration of the NaI. A prototype of the spectrometer was tested at the LANSCE DTL to validate the feasibility of measuring gamma spectra and performing self-calibration in situ. A summary of test results with the LANSCE prototype will be presented, along with a detector system design that aims to be modular and inexpensive across all modules in the DTL. Plans for future development will be presented as well.

Poster WEPA42 [1.308 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-WEPA42

About •

Received ※ 04 August 2022 — Revised ※ 06 August 2022 — Accepted ※ 09 August 2022 — Issue date ※ 11 August 2022

Cite •

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