| Paper | Title | Other Keywords | Page |
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| WEPV007 | Machine Learning Projects at the 1.5-GeV Synchroton Light Source DELTA | controls, storage-ring, synchrotron, electron | 631 |
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| In recent years, several machine learning (ML) based projects have been developed to support automated monitoring and operation of the DELTA electron storage ring facility. This includes self-regulating global and local orbit correction of the stored electron beam, betatron tune feedback as well as electron transfer rate (injection) optimization. Furthermore, the implementation for a ML-based chromaticity control is currently prepared. Some of these processes were initially simulated and then successfully transferred to real machine operation. This report provides an overview of the current status of these projects. | |||
| DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-WEPV007 | ||
| About • | Received ※ 10 October 2021 Accepted ※ 21 November 2021 Issue date ※ 02 February 2022 | ||
| Cite • | reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml) | ||
| WEPV010 | R&D of the KEK Linac Accelerator Tuning Using Machine Learning | linac, network, operation, electron | 640 |
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| We have developed a machine-learning-based operation tuning scheme for the KEK e−/e+ injector linac (Linac), to improve the injection efficiency. The tuning scheme is based on the various accelerator operation data (control parameters, monitoring data and environmental data) of Linac. For the studies, we use the accumulated Linac operation data from 2018 to 2021. To solve the problems on the accelerator tuning of, 1. A lot of parameters (~1000) should be tuned, and these parameters are intricately correlated with each other; and 2. Continuous environmental change, due to temperature change, ground motion, tidal force, etc., affects to the operation tuning; We have developed, 1. Visualization of the accelerator parameters (~1000) trend/correlation distribution based on the dimensionality reduction using Variational Autoencoder (VAE), to see the long-term correlation between the accelerator operation parameters and the environmental data, and 2. Accelerator tuning method using the deep neural network, which is continuously updated with the short-term accelerator data to adapt the environment changes. In this presentation, we report the current status of the R&D. | |||
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Poster WEPV010 [1.997 MB] | ||
| DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-WEPV010 | ||
| About • | Received ※ 10 October 2021 Revised ※ 19 October 2021 Accepted ※ 21 November 2021 Issue date ※ 11 January 2022 | ||
| Cite • | reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml) | ||
| THPV028 | Analysis of AC Line Fluctuation for Timing System at KEK | timing, linac, operation, positron | 923 |
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| The timing system controls the injection procedure of the accelerator by performing signal synchronization and trigger delivery to the devices all over the installations at KEK. The trigger signals is usually generated at the same phase of an AC power line to reduce the unwanted variation of the beam quality. This requirement originates from the power supply systems. However, the AC line synchronization conflicts with the bucket selection process of SuperKEKB low energy ring (LER) which stores the positron beam. The positron beam is firstly injected into a damping ring (DR) to lower the emittance before entering desired RF bucket in LER. A long bucket selection cycle for DR and LER makes it difficult to coincide with AC line every injection pulse. This trouble is solved by grouping several injection pulses into various of injection sequences and manipulating the length of sequences to adjust the AC line arrival timing. Therefore, the timing system is sensitive to drastically AC line fluctuation. The failure of timing system caused by strong AC line fluctuation and solutions are introduced in this work. | |||
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Poster THPV028 [1.010 MB] | ||
| DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-THPV028 | ||
| About • | Received ※ 17 October 2021 Revised ※ 28 October 2021 Accepted ※ 21 November 2021 Issue date ※ 09 December 2021 | ||
| Cite • | reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml) | ||
| THPV040 | New Machine Learning Model Application for the Automatic LHC Collimator Beam-Based Alignment | alignment, flattop, software, operation | 953 |
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A collimation system is installed in the Large Hadron Collider (LHC) to protect its sensitive equipment from unavoidable beam losses. An alignment procedure determines the settings of each collimator, by moving the collimator jaws towards the beam until a characteristic loss pattern, consisting of a sharp rise followed by a slow decay, is observed in downstream beam loss monitors. This indicates that the collimator jaw intercepted the reference beam halo and is thus aligned to the beam. The latest alignment software introduced in 2018 relies on supervised machine learning (ML) to detect such spike patterns in real-time*. This enables the automatic alignment of the collimators with a significant reduction in the alignment time**. This paper analyses the first-use performance of this new software focusing on solutions to the identified bottleneck caused by waiting a fixed duration of time when detecting spikes. It is proposed to replace the supervised ML model with a Long-Short Term Memory model able to detect spikes in time windows of varying lengths, waiting for a variable duration of time determined by the spike itself. This will allow to further speed up the automatic alignment.
*G. Azzopardi et al., "Automatic spike detection in beam loss signals for LHC collimator alignment", NIMA 2019. **G. Azzopardi et al., "Operational Results of LHC collimator alignment using ML", IPAC’19. |
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Poster THPV040 [0.894 MB] | ||
| DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-THPV040 | ||
| About • | Received ※ 08 October 2021 Accepted ※ 21 November 2021 Issue date ※ 10 December 2021 | ||
| Cite • | reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml) | ||