| Paper | Title | Page |
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| TUPORI27 | Preliminary Study on the Implementation of the Orbit Correction to the 100 Mev Proton Linac at KOMAC | 613 |
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Funding: This work has been supported through KOMAC operation fund of KAERI by the Korean government (MIST) At Korea Multipurpose Accelerator Complex (KOMAC), we have been operating a 100 MeV linac consisting of 11 DTLs with several beam position monitors (BPMs) and steering magnets installed for the orbit correction of the proton beam. The orbit correction can be performed through the response matrix between the position measurements from the BPMs and the field strength of the steering magnets. In this work, we will show the calculated response matrix from the simulation results, and describe the detailed plans for the implementation of the orbit correction in the real linac system at KOMAC. |
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| DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-LINAC2022-TUPORI27 | |
| About • | Received ※ 20 August 2022 — Revised ※ 29 August 2022 — Accepted ※ 05 September 2022 — Issue date ※ 15 September 2022 | |
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| TUPORI28 | Injector System Development for 1 MeV/n RFQ at KOMAC | 615 |
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Funding: This work has been supported through the KOMAC operation fund of KAERI by the Korean government (MSIT). A Radiofrequency quadrupole (RFQ) system with 200 MHz frequency and 1 MeV/n output energy is under development at KOMAC (Korea Multi-purpose Accelerator Complex) for multiple purposes such as a test-stand for an ion source and low energy beam transport study, ion beam implantation for semiconductors and polymers and neutron generation for material study. We developed an injector system for the RFQ, which is mainly composed of a 2.45 GHz microwave ion source, low energy beam transport with two solenoids, and a vacuum system with a diagnostic chamber. The RFQ was designed to be able to accelerate the beam with 2.5 mass-to-charge ratios (A/q) but we used the proton beam for an initial test to characterize the injector system. A Detailed describtion of the constructed injector system along with test results will be given in this paper. |
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| DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-LINAC2022-TUPORI28 | |
| About • | Received ※ 22 August 2022 — Revised ※ 26 August 2022 — Accepted ※ 29 August 2022 — Issue date ※ 15 September 2022 | |
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| THPORI02 | Machine Learning for Beam Orbit Correction at KOMAC Accelerator | 848 |
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Funding: This work has been supported through KOMAC op-eration fund of KAERI by Ministry of Science and ICT, the Korean government (KAERI ID no. : 524320-22) There are approaches to apply machine learning (ML) techniques to efficiently operate and optimize particle accelerators. Deep neural networks-based model is applied to experiments, correcting beam orbit through the low energy beam transport at the proton injector test stand. For more complex applications, time-series analysis model is studied to predict beam orbit in the 100-MeV beamline at KOMAC. This paper describes experimental data to train neural networks model, and presents the performance of the machine learning models. |
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| DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-LINAC2022-THPORI02 | |
| About • | Received ※ 25 August 2022 — Revised ※ 01 September 2022 — Accepted ※ 08 September 2022 — Issue date ※ 15 September 2022 | |
| Cite • | reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml) | |