IPAC2021 - List of Authors (Zhukov, A.P.)

Paper	Title	Page
TUPAB328	Machine Learning for Time Series Prediction of an Accelerator Beam to Recognize Equipment Malfunction	2272
	C.C. Peters ORNL RAD, Oak Ridge, Tennessee, USA W. Blokland, D.L. Brown, F. Liu, C.D. Long, D. Lu, P. Ramuhalli, D.E. Womble, J. Zhang, A.P. Zhukov ORNL, Oak Ridge, Tennessee, USA
	Funding: ORNL is managed by UT-Battelle, LLC, under contract DE-AC05- 00OR22725 for the U.S. Department of Energy. The Spallation Neutron Source (SNS) is an accelerator based pulsed neutron source based on a 1 GeV pulsed proton Superconducting Radio Frequency (SRF) linear accelerator (linac). Since beginning high power beam operation in 2006 correlations have been found linking abrupt beam loss events to SRF cavity instabilities. With the planned upgrades to double the beam power we expect increased rates of degradation and the importance of minimizing these beam loss events will become ever more important. To further limit degradation, we are developing machine learning approaches to monitor the beam and to detect, predict and prevent beam loss events. Initial research has shown that precursors to beam loss events are detectable. The initial steps are to use ML-based classification to recognize anomalies and to use Long Short-Term Memory (LSTM) autoencoders to predict beam loss. In this paper, we describe recent progress in applying machine learning for recognizing anomalies and predicting beam loss and present initial results of our research using acquired data from different diagnostics and the Machine Protection System (MPS).
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB328
About •	paper received ※ 23 May 2021 paper accepted ※ 28 May 2021 issue date ※ 15 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPAB319	Open XAL Status Report 2021	3421
	N. Milas, J.F. Esteban Müller, E. Laface, Y. Levinsen ESS, Lund, Sweden T.V. Gorlov, A.P. Shishlo, A.P. Zhukov ORNL, Oak Ridge, Tennessee, USA
	The Open XAL accelerator physics software platform is being developed through international collaboration among several facilities since 2010. The goal of the collaboration is to establish Open XAL as a multi-purpose software platform supporting a broad range of tool and application development in accelerator physics and high-level control (Open XAL also ships with a suite of general-purpose accelerator applications). This paper discusses progress in beam dynamics simulation, new RF models, and updated application framework along with new generic accelerator physics applications. We present the current status of the project, a roadmap for continued development, and an overview of the project status at each participating facility.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB319
About •	paper received ※ 19 May 2021 paper accepted ※ 21 July 2021 issue date ※ 11 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

THXA01	Beyond RMS: Understanding the Evolution of Beam Distributions in High Intensity Linacs	3681
	K.J. Ruisard, A.V. Aleksandrov, S.M. Cousineau, A.P. Shishlo, A.P. Zhukov ORNL, Oak Ridge, Tennessee, USA
	Funding: This work has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. Understanding the evolution of beams with space charge is crucial to design and operation of high intensity linacs. While the community holds a broad understanding of the mechanisms leading to emittance growth and halo formation, there is outstanding discrepancy between measurements and beam evolution models that precludes prediction of halo losses. This may be due in part to insufficient information of the initial beam distribution. This talk will describe work at the SNS Beam Test Facility to directly measure the 6D beam distribution. Full-and-direct 6D measurement has revealed hidden but physically significant dependence between the longitudinal distribution and transverse coordinates. This nonlinear correlation is driven by space charge and reproduced by self-consistent simulation of the RFQ. Omission of this interplane correlation, common when bunches are reconstructed from lower-dimensional measurements, degrades downstream predictions. This talk will also describe the novel diagnostics supporting this work. This includes ongoing improvements to efficiency of the 6D phase space measurement as well as recent achievement of six orders of dynamic range in 2D phase space.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THXA01
About •	paper received ※ 20 May 2021 paper accepted ※ 23 July 2021 issue date ※ 17 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Machine Learning for Time Series Prediction of an Accelerator Beam to Recognize Equipment Malfunction

2272

C.C. Peters
ORNL RAD, Oak Ridge, Tennessee, USA
W. Blokland, D.L. Brown, F. Liu, C.D. Long, D. Lu, P. Ramuhalli, D.E. Womble, J. Zhang, A.P. Zhukov
ORNL, Oak Ridge, Tennessee, USA

Funding: ORNL is managed by UT-Battelle, LLC, under contract DE-AC05- 00OR22725 for the U.S. Department of Energy.
The Spallation Neutron Source (SNS) is an accelerator based pulsed neutron source based on a 1 GeV pulsed proton Superconducting Radio Frequency (SRF) linear accelerator (linac). Since beginning high power beam operation in 2006 correlations have been found linking abrupt beam loss events to SRF cavity instabilities. With the planned upgrades to double the beam power we expect increased rates of degradation and the importance of minimizing these beam loss events will become ever more important. To further limit degradation, we are developing machine learning approaches to monitor the beam and to detect, predict and prevent beam loss events. Initial research has shown that precursors to beam loss events are detectable. The initial steps are to use ML-based classification to recognize anomalies and to use Long Short-Term Memory (LSTM) autoencoders to predict beam loss. In this paper, we describe recent progress in applying machine learning for recognizing anomalies and predicting beam loss and present initial results of our research using acquired data from different diagnostics and the Machine Protection System (MPS).

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB328

About •

paper received ※ 23 May 2021 paper accepted ※ 28 May 2021 issue date ※ 15 August 2021

Export •

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

WEPAB319

Open XAL Status Report 2021

3421

N. Milas, J.F. Esteban Müller, E. Laface, Y. Levinsen
ESS, Lund, Sweden
T.V. Gorlov, A.P. Shishlo, A.P. Zhukov
ORNL, Oak Ridge, Tennessee, USA

The Open XAL accelerator physics software platform is being developed through international collaboration among several facilities since 2010. The goal of the collaboration is to establish Open XAL as a multi-purpose software platform supporting a broad range of tool and application development in accelerator physics and high-level control (Open XAL also ships with a suite of general-purpose accelerator applications). This paper discusses progress in beam dynamics simulation, new RF models, and updated application framework along with new generic accelerator physics applications. We present the current status of the project, a roadmap for continued development, and an overview of the project status at each participating facility.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB319

About •

paper received ※ 19 May 2021 paper accepted ※ 21 July 2021 issue date ※ 11 August 2021

Export •

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

THXA01

Beyond RMS: Understanding the Evolution of Beam Distributions in High Intensity Linacs

3681

K.J. Ruisard, A.V. Aleksandrov, S.M. Cousineau, A.P. Shishlo, A.P. Zhukov
ORNL, Oak Ridge, Tennessee, USA

Funding: This work has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy.
Understanding the evolution of beams with space charge is crucial to design and operation of high intensity linacs. While the community holds a broad understanding of the mechanisms leading to emittance growth and halo formation, there is outstanding discrepancy between measurements and beam evolution models that precludes prediction of halo losses. This may be due in part to insufficient information of the initial beam distribution. This talk will describe work at the SNS Beam Test Facility to directly measure the 6D beam distribution. Full-and-direct 6D measurement has revealed hidden but physically significant dependence between the longitudinal distribution and transverse coordinates. This nonlinear correlation is driven by space charge and reproduced by self-consistent simulation of the RFQ. Omission of this interplane correlation, common when bunches are reconstructed from lower-dimensional measurements, degrades downstream predictions. This talk will also describe the novel diagnostics supporting this work. This includes ongoing improvements to efficiency of the 6D phase space measurement as well as recent achievement of six orders of dynamic range in 2D phase space.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THXA01

About •

paper received ※ 20 May 2021 paper accepted ※ 23 July 2021 issue date ※ 17 August 2021

Export •

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