Accelerator beam phase space tomography using machine learning to account for variations in beamline components
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910449629396992 |
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| author | Wolski, Andrzej Botelho, Diego Dunning, David Pollard, Amelia E. |
| author_facet | Wolski, Andrzej Botelho, Diego Dunning, David Pollard, Amelia E. |
| contents | We describe a technique for reconstruction of the four-dimensional transverse phase space of a beam in an accelerator beamline, taking into account the presence of unknown errors on the strengths of magnets used in the data collection. Use of machine learning allows rapid reconstruction of the phase-space distribution while at the same time providing estimates of the magnet errors. The technique is demonstrated using experimental data from CLARA, an accelerator test facility at Daresbury Laboratory. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_10028 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Accelerator beam phase space tomography using machine learning to account for variations in beamline components Wolski, Andrzej Botelho, Diego Dunning, David Pollard, Amelia E. Accelerator Physics We describe a technique for reconstruction of the four-dimensional transverse phase space of a beam in an accelerator beamline, taking into account the presence of unknown errors on the strengths of magnets used in the data collection. Use of machine learning allows rapid reconstruction of the phase-space distribution while at the same time providing estimates of the magnet errors. The technique is demonstrated using experimental data from CLARA, an accelerator test facility at Daresbury Laboratory. |
| title | Accelerator beam phase space tomography using machine learning to account for variations in beamline components |
| topic | Accelerator Physics |
| url | https://arxiv.org/abs/2405.10028 |