Speaker
Description
This study addresses the challenge of directly measuring the longitudinal phase space in electron storage rings and proposes a novel reconstruction method based on machine learning. Precise reconstruction of the longitudinal phase space distribution is crucial for optimizing beam performance and suppressing instabilities. However, traditional methods used in proton accelerators are unsuitable for electron storage rings due to detector bandwidth limitations, which prevent their application to beams with picosecond-scale lengths.
To overcome this, the proposed method first establishes a mapping dataset through theoretical simulations, linking machine parameters and initial longitudinal phase space distributions to observed transient images during injection. Machine learning models are then trained on this dataset. Ultimately, the trained model can directly reconstruct the initial longitudinal phase space distribution and its key physical parameters from experimentally observed transient injection images. This approach provides a new diagnostic technique for the longitudinal phase space in electron storage rings, with the potential to support the fine-tuning and performance enhancement of next-generation accelerators.
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