Speaker
Description
This study explores the application of machine learning techniques for phase space reconstruction of heavy ion linac beams, a critical aspect of understanding and optimizing beam dynamics for advanced nuclear physics experiments. Modern machine learning methods, including neural networks and differentiable simulations, are employed to reconstruct the multidimensional phase space distribution from limited and noisy measurement data. These methods excel at modeling nonlinear relationships and inferring missing information, addressing traditional challenges in high-dimensional data processing. The framework uses beam diagnostics data, such as beam profiles and time-of-flight measurements, to train predictive models capable of accurately reconstructing spatial, angular, and energy distributions. Preliminary results demonstrate significant improvements in reconstruction accuracy compared to conventional approaches, with potential for real-time implementation. This work underscores the effectiveness of machine learning for beam diagnostics and optimization, offering a pathway to enhanced performance and efficiency in heavy ion linac operations.
| Region represented | Asia |
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| Paper preparation format | LaTeX |
