NAPAC2022 - List of Authors (Einstein-Curtis, J.A.)

Paper	Title	Page
MOPA24	LCLS-II and HE Cryomodule Microphonics at CMTF at Fermilab	103
	C. Contreras-Martinez, B.E. Chase, A.T. Cravatta, J.A. Einstein-Curtis, E.R. Harms, J.P. Holzbauer, J.N. Makara, S. Posen, R. Wang Fermilab, Batavia, Illinois, USA L.R. Doolittle LBNL, Berkeley, California, USA
	Microphonics causes the cavity to detune. This study discusses the microphonics of 16 cryomodules, 14 for LCLS-II and 2 for LCLS-II HE tested at CMTF. The peak detuning, as well as the RMS detuning for each cryomodule, will be discussed. For each cryomodule, the data was taken with enough soaking time to prevent any thermalization effects which can show up in the detuning. Each data capture taken was 30 minutes or longer and sampled at 1 kHz.
	Poster MOPA24 [1.428 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA24
About •	Received ※ 03 August 2022 — Revised ※ 10 August 2022 — Accepted ※ 11 August 2022 — Issue date ※ 20 September 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)

Paper

Title

Page

LCLS-II and HE Cryomodule Microphonics at CMTF at Fermilab

103

C. Contreras-Martinez, B.E. Chase, A.T. Cravatta, J.A. Einstein-Curtis, E.R. Harms, J.P. Holzbauer, J.N. Makara, S. Posen, R. Wang
Fermilab, Batavia, Illinois, USA
L.R. Doolittle
LBNL, Berkeley, California, USA

Microphonics causes the cavity to detune. This study discusses the microphonics of 16 cryomodules, 14 for LCLS-II and 2 for LCLS-II HE tested at CMTF. The peak detuning, as well as the RMS detuning for each cryomodule, will be discussed. For each cryomodule, the data was taken with enough soaking time to prevent any thermalization effects which can show up in the detuning. Each data capture taken was 30 minutes or longer and sampled at 1 kHz.

Poster MOPA24 [1.428 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA24

About •

Received ※ 03 August 2022 — Revised ※ 10 August 2022 — Accepted ※ 11 August 2022 — Issue date ※ 20 September 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)