Efficient accelerator operation with artificial intelligence based optimization methods
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| Format: | Recurso digital |
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2025
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| _version_ | 1866901458306203648 |
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| author | Matzoukas, Evangelos Xu, Chenran Blomley, Edmund Bruendermann, Erik De Carne, Giovanni Gethmann, Julian Müller, Anke-Susanne |
| author_facet | Matzoukas, Evangelos Xu, Chenran Blomley, Edmund Bruendermann, Erik De Carne, Giovanni Gethmann, Julian Müller, Anke-Susanne |
| contents | <p>Tuning injections is a challenging task for the operation of accelerator facilities and synchrotron light sources, particularly during the commissioning phase. Efficient tuning of the transfer line is essential for ensuring optimal beam transport and injection efficiency. This process is further complicated by challenges such as beam misalignment in quadrupole magnets, which can degrade beam quality and disrupt operations. Traditional tuning methods are often time-consuming and insufficient for addressing the complexities of high-dimensional paramter spaces. In this work, we explore the use of advanced AI methods, including Bayesian optimization, to automate and improve the tuning process. Initial results, demonstrated on the transfer line of KARA (Karlsruhe Research Accelerator) at KIT (Karlsruhe Institute of Technology), show promising improvements in beam alignment and transport efficiency, representing first steps toward more efficient and reliable accelerator operation. This study is part of the RF2.0 project, funded by the Horizon Europe program of the European Commission, whcih focuses on advancing energy-efficient solutions for particle accelerators.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_18429_JACoW-IPAC2025-THPM018 |
| institution | Zenodo |
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| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Efficient accelerator operation with artificial intelligence based optimization methods Matzoukas, Evangelos Xu, Chenran Blomley, Edmund Bruendermann, Erik De Carne, Giovanni Gethmann, Julian Müller, Anke-Susanne bax beam tuning injection line Machine learning Particle accelerator bayesian optimization <p>Tuning injections is a challenging task for the operation of accelerator facilities and synchrotron light sources, particularly during the commissioning phase. Efficient tuning of the transfer line is essential for ensuring optimal beam transport and injection efficiency. This process is further complicated by challenges such as beam misalignment in quadrupole magnets, which can degrade beam quality and disrupt operations. Traditional tuning methods are often time-consuming and insufficient for addressing the complexities of high-dimensional paramter spaces. In this work, we explore the use of advanced AI methods, including Bayesian optimization, to automate and improve the tuning process. Initial results, demonstrated on the transfer line of KARA (Karlsruhe Research Accelerator) at KIT (Karlsruhe Institute of Technology), show promising improvements in beam alignment and transport efficiency, representing first steps toward more efficient and reliable accelerator operation. This study is part of the RF2.0 project, funded by the Horizon Europe program of the European Commission, whcih focuses on advancing energy-efficient solutions for particle accelerators.</p> |
| title | Efficient accelerator operation with artificial intelligence based optimization methods |
| topic | bax beam tuning injection line Machine learning Particle accelerator bayesian optimization |
| url | https://doi.org/10.18429/JACoW-IPAC2025-THPM018 |