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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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Lenguaje: | eng |
Publicado: |
2020
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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 The current program is outdated, complex and hard to develop further.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 The current program is outdated, complex and hard to develop further.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 |