HB2023 - List of Authors (Park, C.S.)

Paper	Title	Page
WEA3C3	Differential Algebra for Accelerator Optimization with Truncated Green’s Function	254
	C.S. Park Korea University Sejong Campus, Sejong, Republic of Korea
	Accelerator optimization is a critical problem in the design of high-performance particle accelerators. The truncated Green’s function space charge algorithm is a powerful tool for simulating the effects of space charge in accelerators. However, the truncated Green’s function algorithm can be computationally expensive, especially for large accelerators. In this work, we present a new approach to accelerator optimization using differential algebra with the truncated Green’s function space charge algorithm. Our approach uses differential algebra to symbolically represent the equations of the truncated Green’s function algorithm. This allows us to perform efficient symbolic analysis of the equations, which can be used to identify and optimize the accelerator parameters. We demonstrate the effectiveness of our approach by applying it to the optimization of a linear accelerator. We show that our approach can significantly reduce the computational cost of the truncated Green’s function algorithm, while still achieving high accuracy.
	Slides WEA3C3 [0.772 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-HB2023-WEA3C3
About •	Received ※ 28 September 2023 — Revised ※ 11 October 2023 — Accepted ※ 14 October 2023 — Issued ※ 18 October 2023
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Differential Algebra for Accelerator Optimization with Truncated Green’s Function

254

C.S. Park
Korea University Sejong Campus, Sejong, Republic of Korea

Accelerator optimization is a critical problem in the design of high-performance particle accelerators. The truncated Green’s function space charge algorithm is a powerful tool for simulating the effects of space charge in accelerators. However, the truncated Green’s function algorithm can be computationally expensive, especially for large accelerators. In this work, we present a new approach to accelerator optimization using differential algebra with the truncated Green’s function space charge algorithm. Our approach uses differential algebra to symbolically represent the equations of the truncated Green’s function algorithm. This allows us to perform efficient symbolic analysis of the equations, which can be used to identify and optimize the accelerator parameters. We demonstrate the effectiveness of our approach by applying it to the optimization of a linear accelerator. We show that our approach can significantly reduce the computational cost of the truncated Green’s function algorithm, while still achieving high accuracy.

Slides WEA3C3 [0.772 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-HB2023-WEA3C3

About •

Received ※ 28 September 2023 — Revised ※ 11 October 2023 — Accepted ※ 14 October 2023 — Issued ※ 18 October 2023

Cite •

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