Improving beam simulations as well as machine and target protection in the SINQ beam line at PSI-HIPA

Reggiani, D.; Blau, B.; Dölling, R.; Duperrex, P.A.; Kiselev, D.; Talanov, V.; Welte, J.; Wohlmuther, M.

doi:10.3233/jnr-200162

Cited by 5 publications

(3 citation statements)

References 2 publications

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“…The HIPA at the Paul Scherrer Institut (PSI) is one of the most powerful proton cyclotron facilities in the world, with nearly 1.4 MW of beam power [20]. The HIPA Archiver is an in-house developed archiver software that stores values of EPICS [21] Process Variables, i.e., channels, according to tailored configuration settings [22].…”

Section: Dataset and Preprocessingmentioning

confidence: 99%

A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators

et al. 2021

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The beam interruptions (interlocks) of particle accelerators, despite being necessary safety measures, lead to abrupt operational changes and a substantial loss of beam time. A novel time series classification approach is applied to decrease beam time loss in the High-Intensity Proton Accelerator complex by forecasting interlock events. The forecasting is performed through binary classification of windows of multivariate time series. The time series are transformed into Recurrence Plots which are then classified by a Convolutional Neural Network, which not only captures the inner structure of the time series but also uses the advances of image classification techniques. Our best-performing interlock-to-stable classifier reaches an Area under the ROC Curve value of 0.71±0.01 compared to 0.65±0.01 of a Random Forest model, and it can potentially reduce the beam time loss by 0.5±0.2 s per interlock.

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Section: Dataset and Preprocessingmentioning

confidence: 99%

A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators

et al. 2021

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Section: Dataset and Preprocessingmentioning

confidence: 99%

A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators

Li,

Zacharias,

Snuverink

et al. 2021

Preprint

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The beam interruptions (interlocks) of particle accelerators, despite being necessary safety measures, lead to abrupt operational changes and a substantial loss of beam time. A novel time series classification approach is applied to decrease beam time loss in the High Intensity Proton Accelerator complex by forecasting interlock events. The forecasting is performed through binary classification of windows of multivariate time series. The time series are transformed into Recurrence Plots which are then classified by a Convolutional Neural Network, which not only captures the inner structure of the time series but also utilizes the advances of image classification techniques. Our best performing interlock-to-stable classifier reaches an Area under the ROC Curve value of 0.71 ± 0.01 compared to 0.65 ± 0.01 of a Random Forest model, and it can potentially reduce the beam time loss by 0.5 ± 0.2 seconds per interlock.

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“…We applied the previously described model to data from the HIPA diagnostic devices. HIPA is one of the most powerful cyclotrons in the world, able to provide a 1.4 MW beam to several experiments [17]. To operate and monitor this facility, about 180,000 signals are generated by devices of very different nature, of which about 19,000 are stored in a database (the Archiver).…”

Section: Data Collection and Exploitationmentioning

confidence: 99%

Experience with anomaly detection using ensemble models on streaming data at HIPA

Portugal

Snuverink

2021

Nuclear Instruments and Methods in Physics Research Section A:

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Improving beam simulations as well as machine and target protection in the SINQ beam line at PSI-HIPA

Cited by 5 publications

References 2 publications

A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators

A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators

A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators

Experience with anomaly detection using ensemble models on streaming data at HIPA

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