JuTrack: a Julia package for auto-differentiable accelerator modeling and particle tracking
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916543064965120 |
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| author | Wan, Jinyu Alamprese, Helena Ratcliff, Christian Qiang, Ji Hao, Yue |
| author_facet | Wan, Jinyu Alamprese, Helena Ratcliff, Christian Qiang, Ji Hao, Yue |
| contents | Efficient accelerator modeling and particle tracking are key for the design and configuration of modern particle accelerators. In this work, we present JuTrack, a nested accelerator modeling package developed in the Julia programming language and enhanced with compiler-level automatic differentiation (AD). With the aid of AD, JuTrack enables rapid derivative calculations in accelerator modeling, facilitating sensitivity analyses and optimization tasks. We demonstrate the effectiveness of AD-derived derivatives through several practical applications, including sensitivity analysis of space-charge-induced emittance growth, nonlinear beam dynamics analysis for a synchrotron light source, and lattice parameter tuning of the future Electron-Ion Collider (EIC). Through the incorporation of automatic differentiation, this package opens up new possibilities for accelerator physicists in beam physics studies and accelerator design optimization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_20522 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | JuTrack: a Julia package for auto-differentiable accelerator modeling and particle tracking Wan, Jinyu Alamprese, Helena Ratcliff, Christian Qiang, Ji Hao, Yue Accelerator Physics Efficient accelerator modeling and particle tracking are key for the design and configuration of modern particle accelerators. In this work, we present JuTrack, a nested accelerator modeling package developed in the Julia programming language and enhanced with compiler-level automatic differentiation (AD). With the aid of AD, JuTrack enables rapid derivative calculations in accelerator modeling, facilitating sensitivity analyses and optimization tasks. We demonstrate the effectiveness of AD-derived derivatives through several practical applications, including sensitivity analysis of space-charge-induced emittance growth, nonlinear beam dynamics analysis for a synchrotron light source, and lattice parameter tuning of the future Electron-Ion Collider (EIC). Through the incorporation of automatic differentiation, this package opens up new possibilities for accelerator physicists in beam physics studies and accelerator design optimization. |
| title | JuTrack: a Julia package for auto-differentiable accelerator modeling and particle tracking |
| topic | Accelerator Physics |
| url | https://arxiv.org/abs/2409.20522 |