ICALEPCS2023 - List of Authors (Valette, M.)

Paper	Title	Page
TUPDP117	Classification and Prediction of Superconducting Magnet Quenches	856
	J.A. Einstein-Curtis, J.P. Edelen, M.C. Kilpatrick, R. O’Rourke RadiaSoft LLC, Boulder, Colorado, USA K.A. Drees, J.S. Laster, M. Valette BNL, Upton, New York, USA
	Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of High Energy Physics under Award Number DE-SC0021699. Robust and reliable quench detection for superconducting magnets is increasingly important as facilities push the boundaries of intensity and operational runtime. RadiaSoft has been working with Brookhaven National Lab on quench detection and prediction for superconducting magnets installed in the RHIC storage rings. This project has analyzed several years of power supply and beam position monitor data to train automated classification tools and automated quench precursor determination based on input sequences. Classification was performed using supervised multilayer perceptron and boosted decision tree architectures, while models of the expected operation of the ring were developed using a variety of autoencoder architectures. We have continued efforts to maximize area under the receiver operating characteristic curve for the multiple classification problem of real quench, fake quench, and no-quench events. We have also begun work on long short-term memory (LSTM) and other recurrent architectures for quench prediction. Examinations of future work utilizing more robust architectures, such as variational autoencoders and Siamese models, as well as methods necessary for uncertainty quantification will be discussed.
DOI •	reference for this paper ※ doi:10.18429/JACoW-ICALEPCS2023-TUPDP117
About •	Received ※ 08 October 2023 — Revised ※ 22 October 2023 — Accepted ※ 05 December 2023 — Issued ※ 07 December 2023
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Classification and Prediction of Superconducting Magnet Quenches

856

J.A. Einstein-Curtis, J.P. Edelen, M.C. Kilpatrick, R. O’Rourke
RadiaSoft LLC, Boulder, Colorado, USA
K.A. Drees, J.S. Laster, M. Valette
BNL, Upton, New York, USA

Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of High Energy Physics under Award Number DE-SC0021699.
Robust and reliable quench detection for superconducting magnets is increasingly important as facilities push the boundaries of intensity and operational runtime. RadiaSoft has been working with Brookhaven National Lab on quench detection and prediction for superconducting magnets installed in the RHIC storage rings. This project has analyzed several years of power supply and beam position monitor data to train automated classification tools and automated quench precursor determination based on input sequences. Classification was performed using supervised multilayer perceptron and boosted decision tree architectures, while models of the expected operation of the ring were developed using a variety of autoencoder architectures. We have continued efforts to maximize area under the receiver operating characteristic curve for the multiple classification problem of real quench, fake quench, and no-quench events. We have also begun work on long short-term memory (LSTM) and other recurrent architectures for quench prediction. Examinations of future work utilizing more robust architectures, such as variational autoencoders and Siamese models, as well as methods necessary for uncertainty quantification will be discussed.

DOI •

reference for this paper ※ doi:10.18429/JACoW-ICALEPCS2023-TUPDP117

About •

Received ※ 08 October 2023 — Revised ※ 22 October 2023 — Accepted ※ 05 December 2023 — Issued ※ 07 December 2023

Cite •

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