NAPAC2022 - List of Keywords (framework)

Paper	Title	Other Keywords	Page
MOPA55	Facilitating Machine Learning Collaborations Between Labs, Universities, and Industry	controls, simulation, software, operation	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)

TUYE3	An Open-Source Based Data Management and Processing Framework on a Central Server for Scientific Experimental Data	experiment, software, data-management, network	307
	A. Liu, J.R. Callahan, S. Poddar, W. Si Euclid TechLabs, Solon, Ohio, USA J. Gao AJS Smartech LLC, Naperville, TX, USA
	Funding: This work is supported by the US DOE SBIR program under contract number DE-SC0021512. The ever-expanding size of accelerator operation and experimental data including those generated by electron microscopes and beamline facilities renders most proprietary software inefficient at managing data. The Findability, Accessibility, Interoperability, and Reuse (FAIR) principles of digital assets require a convenient platform for users to share and manage data on. An open-source data framework for storing raw data and metadata, hosting databases, and providing a platform for data processing and visualization is highly desirable. In this paper, we present an open-source, infrastructure-independent data management software framework, named by Euclid-NexusLIMS, to archive, register, record, visualize and process experimental data. The software was targeted initially for electron microscopes, but can be widely applied to all scientific experimental data.
	Slides TUYE3 [5.891 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUYE3
About •	Received ※ 04 August 2022 — Revised ※ 07 August 2022 — Accepted ※ 08 August 2022 — Issue date ※ 24 August 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPA16	Singularity-Free Exact Dipole Bend Transport Equations	dipole, simulation, lattice, GUI	375
	D. Sagan Cornell University (CLASSE), Cornell Laboratory for Accelerator-Based Sciences and Education, Ithaca, New York, USA
	Funding: Department of Energy Exact transport equations for a pure dipole bend (a bend with a dipole field and nothing else) have been derived and formulated to avoid singularities when evaluated. The transport equations include finite edge angles and no assumption is made in terms of the bending field being matched to the curvature of the coordinate system.
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA16
About •	Received ※ 05 August 2022 — Revised ※ 09 August 2022 — Accepted ※ 10 August 2022 — Issue date ※ 16 September 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Other Keywords

Page

MOPA55

Facilitating Machine Learning Collaborations Between Labs, Universities, and Industry

controls, simulation, software, operation

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)

TUYE3

An Open-Source Based Data Management and Processing Framework on a Central Server for Scientific Experimental Data

experiment, software, data-management, network

307

A. Liu, J.R. Callahan, S. Poddar, W. Si
Euclid TechLabs, Solon, Ohio, USA
J. Gao
AJS Smartech LLC, Naperville, TX, USA

Funding: This work is supported by the US DOE SBIR program under contract number DE-SC0021512.
The ever-expanding size of accelerator operation and experimental data including those generated by electron microscopes and beamline facilities renders most proprietary software inefficient at managing data. The Findability, Accessibility, Interoperability, and Reuse (FAIR) principles of digital assets require a convenient platform for users to share and manage data on. An open-source data framework for storing raw data and metadata, hosting databases, and providing a platform for data processing and visualization is highly desirable. In this paper, we present an open-source, infrastructure-independent data management software framework, named by Euclid-NexusLIMS, to archive, register, record, visualize and process experimental data. The software was targeted initially for electron microscopes, but can be widely applied to all scientific experimental data.

Slides TUYE3 [5.891 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUYE3

About •

Received ※ 04 August 2022 — Revised ※ 07 August 2022 — Accepted ※ 08 August 2022 — Issue date ※ 24 August 2022

Cite •

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

TUPA16

Singularity-Free Exact Dipole Bend Transport Equations

dipole, simulation, lattice, GUI

375

D. Sagan
Cornell University (CLASSE), Cornell Laboratory for Accelerator-Based Sciences and Education, Ithaca, New York, USA

Funding: Department of Energy
Exact transport equations for a pure dipole bend (a bend with a dipole field and nothing else) have been derived and formulated to avoid singularities when evaluated. The transport equations include finite edge angles and no assumption is made in terms of the bending field being matched to the curvature of the coordinate system.

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA16

About •

Received ※ 05 August 2022 — Revised ※ 09 August 2022 — Accepted ※ 10 August 2022 — Issue date ※ 16 September 2022

Cite •

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