IPAC2022 - List of Authors (Grindheim, C.H.)

Paper	Title	Page
MOPOPT043	Recent Developments in Longitudinal Phase Space Tomography	347
	S.C.P. Albright, A. Lasheen CERN, Meyrin, Switzerland C.H. Grindheim NTNU, Trondheim, Norway A.H.C. Lu KTH/NADA, Stockholm, Sweden
	Longitudinal phase space tomography has been a mainstay of longitudinal beam diagnostics in most of the CERN synchrotrons for over two decades. Originally, the reconstructions were performed by a highly optimised Fortran implementation. To facilitate increased flexibility, and leveraging the significant increase in computing power since the original development, a new version of the reconstruction code has been developed. This implements an object-oriented Python API, with the computationally heavy calculations in C++ for improved performance. The Python/C++ implementation is designed to be highly modular, enabling new and diverse use cases. For example, the macro-particle tracking for the tomography can now be performed externally, or a single set of tracked particles can be reused for multiple reconstructions. This paper summarises the features of the new implementation, and some of the key applications that have been enabled as a result.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-MOPOPT043
About •	Received ※ 30 May 2022 — Revised ※ 12 June 2022 — Accepted ※ 12 June 2022 — Issue date ※ 13 June 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Recent Developments in Longitudinal Phase Space Tomography

347

S.C.P. Albright, A. Lasheen
CERN, Meyrin, Switzerland
C.H. Grindheim
NTNU, Trondheim, Norway
A.H.C. Lu
KTH/NADA, Stockholm, Sweden

Longitudinal phase space tomography has been a mainstay of longitudinal beam diagnostics in most of the CERN synchrotrons for over two decades. Originally, the reconstructions were performed by a highly optimised Fortran implementation. To facilitate increased flexibility, and leveraging the significant increase in computing power since the original development, a new version of the reconstruction code has been developed. This implements an object-oriented Python API, with the computationally heavy calculations in C++ for improved performance. The Python/C++ implementation is designed to be highly modular, enabling new and diverse use cases. For example, the macro-particle tracking for the tomography can now be performed externally, or a single set of tracked particles can be reused for multiple reconstructions. This paper summarises the features of the new implementation, and some of the key applications that have been enabled as a result.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-MOPOPT043

About •

Received ※ 30 May 2022 — Revised ※ 12 June 2022 — Accepted ※ 12 June 2022 — Issue date ※ 13 June 2022

Cite •

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