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A Python Library for Longitudinal Beam Tomography

Measuring the longitudinal phase space distribution of particle beams is needed for a myriad of purposes. Among these are machine calibration and bunch quality assessment. Since the tomographic reconstruction algorithm was developed, it has been a vital part of operation at CERN. The current pr...

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Autor principal: Grindheim, Christoffer Hjerto
Lenguaje:eng
Publicado: 2020
Materias:
Acceso en línea:http://cds.cern.ch/record/2717822
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author Grindheim, Christoffer Hjerto
author_facet Grindheim, Christoffer Hjerto
author_sort Grindheim, Christoffer Hjerto
collection CERN
description Measuring the longitudinal phase space distribution of particle beams is needed for a myriad of purposes. Among these are machine calibration and bunch quality assessment. Since the tomographic reconstruction algorithm was developed, it has been a vital part of operation at CERN. The current program is outdated, complex and hard to develop further. Its functionality should be able to be customised for each machine, and for the needs of the individual users. For this reason, a new version must be developed, able to serve the wide range of requirements the future might bring. This dissertation describes the translation from a program written in highly optimised Fortran95 code, to a modular Python library. It also addresses experiments with a hardware accelerated implementation of the tomographic reconstruction routine. Finally, the code is profiled with respect to both output quality and run time, and it is confirmed that it is ready for operational usage.
id cern-2717822
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2020
record_format invenio
spelling cern-27178222020-07-21T13:55:13Zhttp://cds.cern.ch/record/2717822engGrindheim, Christoffer HjertoA Python Library for Longitudinal Beam TomographyComputing and ComputersAccelerators and Storage RingsMeasuring the longitudinal phase space distribution of particle beams is needed for a myriad of purposes. Among these are machine calibration and bunch quality assessment. Since the tomographic reconstruction algorithm was developed, it has been a vital part of operation at CERN. The current program is outdated, complex and hard to develop further. Its functionality should be able to be customised for each machine, and for the needs of the individual users. For this reason, a new version must be developed, able to serve the wide range of requirements the future might bring. This dissertation describes the translation from a program written in highly optimised Fortran95 code, to a modular Python library. It also addresses experiments with a hardware accelerated implementation of the tomographic reconstruction routine. Finally, the code is profiled with respect to both output quality and run time, and it is confirmed that it is ready for operational usage.CERN-THESIS-2020-031oai:cds.cern.ch:27178222020-05-12T08:08:31Z
spellingShingle Computing and Computers
Accelerators and Storage Rings
Grindheim, Christoffer Hjerto
A Python Library for Longitudinal Beam Tomography
title A Python Library for Longitudinal Beam Tomography
title_full A Python Library for Longitudinal Beam Tomography
title_fullStr A Python Library for Longitudinal Beam Tomography
title_full_unstemmed A Python Library for Longitudinal Beam Tomography
title_short A Python Library for Longitudinal Beam Tomography
title_sort python library for longitudinal beam tomography
topic Computing and Computers
Accelerators and Storage Rings
url http://cds.cern.ch/record/2717822
work_keys_str_mv AT grindheimchristofferhjerto apythonlibraryforlongitudinalbeamtomography
AT grindheimchristofferhjerto pythonlibraryforlongitudinalbeamtomography