N-dimensional maximum-entropy tomography via particle sampling
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866912374964879360 |
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| author | Hoover, Austin |
| author_facet | Hoover, Austin |
| contents | We propose a modified maximum-entropy (MENT) algorithm for six-dimensional phase space tomography. The algorithm uses particle sampling and low-dimensional density estimation to approximate large sets of high-dimensional integrals in the original MENT formulation. We implement this approach using Markov Chain Monte Carlo (MCMC) sampling techniques and demonstrate convergence of six-dimensional MENT on both synthetic and measured data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_17915 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | N-dimensional maximum-entropy tomography via particle sampling Hoover, Austin Accelerator Physics Data Analysis, Statistics and Probability We propose a modified maximum-entropy (MENT) algorithm for six-dimensional phase space tomography. The algorithm uses particle sampling and low-dimensional density estimation to approximate large sets of high-dimensional integrals in the original MENT formulation. We implement this approach using Markov Chain Monte Carlo (MCMC) sampling techniques and demonstrate convergence of six-dimensional MENT on both synthetic and measured data. |
| title | N-dimensional maximum-entropy tomography via particle sampling |
| topic | Accelerator Physics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2409.17915 |