pyTRAIN -- a modern TRAIN implementation
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917423113830400 |
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| author | Hostettler, Michi Buffat, Xavier Persson, Tobias Pieloni, Tatiana Wenninger, Jorg |
| author_facet | Hostettler, Michi Buffat, Xavier Persson, Tobias Pieloni, Tatiana Wenninger, Jorg |
| contents | The TRAIN code, developed in 1995 as a post-processor for second-order transport maps from MAD, has been used extensively at the LEP and the LHC to study self-consistent closed orbits, tunes and chromaticities of bunch trains under the presence of beam-beam long-range (BBLR) and PACMAN effects.. This paper presents a modern re-implementation of the TRAIN concept in Python using well-known numeric libraries (numpy, scipy) and an optional link to MAD-X via cpymad. This greatly improves the usability, maintainability and extensibility of the code. New functionality includes the support for arbitrary particle types, an arbitrary number and distribution of beam-beam interaction points, and the extrapolation of the beam-beam induced closed-orbit effects to arbitrary points in the machine. The code is benchmarked against the classic TRAIN code, and simulation results are compared to observations from LHC physics operation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_18466 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | pyTRAIN -- a modern TRAIN implementation Hostettler, Michi Buffat, Xavier Persson, Tobias Pieloni, Tatiana Wenninger, Jorg Accelerator Physics The TRAIN code, developed in 1995 as a post-processor for second-order transport maps from MAD, has been used extensively at the LEP and the LHC to study self-consistent closed orbits, tunes and chromaticities of bunch trains under the presence of beam-beam long-range (BBLR) and PACMAN effects.. This paper presents a modern re-implementation of the TRAIN concept in Python using well-known numeric libraries (numpy, scipy) and an optional link to MAD-X via cpymad. This greatly improves the usability, maintainability and extensibility of the code. New functionality includes the support for arbitrary particle types, an arbitrary number and distribution of beam-beam interaction points, and the extrapolation of the beam-beam induced closed-orbit effects to arbitrary points in the machine. The code is benchmarked against the classic TRAIN code, and simulation results are compared to observations from LHC physics operation. |
| title | pyTRAIN -- a modern TRAIN implementation |
| topic | Accelerator Physics |
| url | https://arxiv.org/abs/2604.18466 |