LINAC2022 - List of Authors (Kim, D.-H.)

Paper	Title	Page
TUPORI27	Preliminary Study on the Implementation of the Orbit Correction to the 100 Mev Proton Linac at KOMAC	613
	S. Lee, J.J. Dang, D.-H. Kim, H.S. Kim, H.-J. Kwon, S.P. Yun KOMAC, KAERI, Gyeongju, Republic of Korea
	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.
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
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPORI28	Injector System Development for 1 MeV/n RFQ at KOMAC	615
	H.S. Kim KAERI, Daejon, Republic of Korea J.J. Dang, D.-H. Kim, H.-J. Kwon, S. Lee, S.P. Yun KOMAC, KAERI, Gyeongju, Republic of Korea
	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.
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
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

THPOPA19	Initial High Power RF Driving Test Using Digital LLRF for RF Conditioning of 1 MeV/n RFQ at KOMAC	781
	H.S. Jeong, W.-H. Jung, D.-H. Kim, H.S. Kim, J.H. Kim, K.H. Kim, S.G. Kim, H.-J. Kwon, P. Lee, Y.G. Song KOMAC, KAERI, Gyeongju, Republic of Korea
	Funding: This work was supported through the KOMAC operation fund by the Ministry of Science and ICT of Korean government. As a part of R&D toward the RFQ based heavy ion irradiation system, the 1 MeV/n RFQ was designed, brazed, installed and commissioned by staff researchers and engineers at KOMAC of KAERI. This 1 MeV/n RFQ system includes the microwave ion source, EBIS, RFQ, quadrupole magnets, switching magnet and the target systems. The digital based Low-Level RF was developed to provide the stable accelerating field to the RFQ. This Low-Level RF has features such as direct RF detection/generation without mixer, non-IQ sampling, PI feedback control, iterative learning based feed-forward control, and the digital RF interlock. In this paper, the characteristics of Low-Level RF are described, as well as the processes and results of an initial RF driving test for the RFQ’s RF conditioning.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-LINAC2022-THPOPA19
About •	Received ※ 22 August 2022 — Revised ※ 28 August 2022 — Accepted ※ 29 August 2022 — Issue date ※ 15 September 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

THPORI02	Machine Learning for Beam Orbit Correction at KOMAC Accelerator	848
	D.-H. Kim, J.J. Dang, H.S. Kim, H.-J. Kwon, S. Lee, S.P. Yun KOMAC, KAERI, Gyeongju, Republic of Korea
	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.
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)

Paper

Title

Page

Preliminary Study on the Implementation of the Orbit Correction to the 100 Mev Proton Linac at KOMAC

613

S. Lee, J.J. Dang, D.-H. Kim, H.S. Kim, H.-J. Kwon, S.P. Yun
KOMAC, KAERI, Gyeongju, Republic of Korea

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.

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

Cite •

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

TUPORI28

Injector System Development for 1 MeV/n RFQ at KOMAC

615

H.S. Kim
KAERI, Daejon, Republic of Korea
J.J. Dang, D.-H. Kim, H.-J. Kwon, S. Lee, S.P. Yun
KOMAC, KAERI, Gyeongju, Republic of Korea

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.

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

Cite •

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

THPOPA19

Initial High Power RF Driving Test Using Digital LLRF for RF Conditioning of 1 MeV/n RFQ at KOMAC

781

H.S. Jeong, W.-H. Jung, D.-H. Kim, H.S. Kim, J.H. Kim, K.H. Kim, S.G. Kim, H.-J. Kwon, P. Lee, Y.G. Song
KOMAC, KAERI, Gyeongju, Republic of Korea

Funding: This work was supported through the KOMAC operation fund by the Ministry of Science and ICT of Korean government.
As a part of R&D toward the RFQ based heavy ion irradiation system, the 1 MeV/n RFQ was designed, brazed, installed and commissioned by staff researchers and engineers at KOMAC of KAERI. This 1 MeV/n RFQ system includes the microwave ion source, EBIS, RFQ, quadrupole magnets, switching magnet and the target systems. The digital based Low-Level RF was developed to provide the stable accelerating field to the RFQ. This Low-Level RF has features such as direct RF detection/generation without mixer, non-IQ sampling, PI feedback control, iterative learning based feed-forward control, and the digital RF interlock. In this paper, the characteristics of Low-Level RF are described, as well as the processes and results of an initial RF driving test for the RFQ’s RF conditioning.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-LINAC2022-THPOPA19

About •

Received ※ 22 August 2022 — Revised ※ 28 August 2022 — Accepted ※ 29 August 2022 — Issue date ※ 15 September 2022

Cite •

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

THPORI02

Machine Learning for Beam Orbit Correction at KOMAC Accelerator

848

D.-H. Kim, J.J. Dang, H.S. Kim, H.-J. Kwon, S. Lee, S.P. Yun
KOMAC, KAERI, Gyeongju, Republic of Korea

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.

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)