IPAC2021 - List of Authors (Long, C.D.)

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
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FRXC04	Time-Resolved H⁻ Beam Emittance Measurement at the SNS Linac Using a Laser Comb	4545
	Y. Liu, A.V. Aleksandrov, C.D. Long ORNL, Oak Ridge, Tennessee, USA
	Funding: This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). We proposed and demonstrated a novel technique to measure time-resolved transverse emittances of the hydrogen ion (H⁻) beam in a 1-GeV high-power accelerator. The measurement is performed in a non-intrusive manner by using laser comb - laser pulses with controllable multi-layer temporal structure generated from a fiber-based master laser oscillator and diode-pumped solid-state laser amplifiers. The technique has been applied to the transverse emittance measurement of 1-GeV H⁻ beam at the Spallation Neutron Source (SNS) high energy beam transport (HEBT). More than 20 time-resolved emittances have been simultaneously measured within a macro-pulse, a single mini-pulse, or a single bunch of the 1.4-MW neutron production H⁻ beam from one measurement.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-FRXC04
About •	paper received ※ 18 May 2021 paper accepted ※ 08 July 2021 issue date ※ 20 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)

FRXC04

Time-Resolved H⁻ Beam Emittance Measurement at the SNS Linac Using a Laser Comb

4545

Y. Liu, A.V. Aleksandrov, C.D. Long
ORNL, Oak Ridge, Tennessee, USA

Funding: This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE).
We proposed and demonstrated a novel technique to measure time-resolved transverse emittances of the hydrogen ion (H⁻) beam in a 1-GeV high-power accelerator. The measurement is performed in a non-intrusive manner by using laser comb - laser pulses with controllable multi-layer temporal structure generated from a fiber-based master laser oscillator and diode-pumped solid-state laser amplifiers. The technique has been applied to the transverse emittance measurement of 1-GeV H⁻ beam at the Spallation Neutron Source (SNS) high energy beam transport (HEBT). More than 20 time-resolved emittances have been simultaneously measured within a macro-pulse, a single mini-pulse, or a single bunch of the 1.4-MW neutron production H⁻ beam from one measurement.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-FRXC04

About •

paper received ※ 18 May 2021 paper accepted ※ 08 July 2021 issue date ※ 20 August 2021

Export •

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