HB2025 - the 71st ICFA Advanced Beam Dynamics workshop on High-Intensity and High-Brightness Hadron Beams

Session

WECBC WGE contributed oral

WECBC

Oct 22, 2025, 11:30 AM

room C (conference center)

Conveners

WECBC WGE contributed oral: WECBC

• Zhijun Wang (Institute of Modern Physics, Chinese Academy of Sciences)

13. A robust and redundant design of the fast protection system for CSNS-II (WECBC01)

peng zhu (Institute of High Energy Physics)

10/22/25, 11:30 AM

WGE: Beam Instrumentation, beam Interaction and AI technology

Contributed Oral Presentation

The Fast Protection System (FPS) is critical for ensuring the efficient and safe operation of the large-scale scientific facility at the China Spallation Neutron Source (CSNS). To address the enhanced requirements of CSNS-II, an advanced FPS architecture has been developed, leveraging high-performance field-programmable gate arrays (FPGAs), high-speed rocket IO serial communication links, and...

126. Quantifying Differences Between High-Dimensional Beam Phase Space Distributions Using f-Divergences​ (WECBC02)

Yu Du (Institute of Modern Physics, Chinese Academy of Sciences)

10/22/25, 11:50 AM

WGE: Beam Instrumentation, beam Interaction and AI technology

Contributed Oral Presentation

Quantifying differences between high-dimensional phase space distributions is essential for analyzing beam measurements and simulations. While f-divergences such as KL or JS divergence are increasingly used for this purpose, including in machine learning applications, their values lack physical interpretability. This work establishes the first physics-grounded framework for f-divergences in...

60. Surrogate modeling of beam dynamics with physics constraints and uncertainty quantification​ (WECBC03)

yaxin hu (Institute of Modern Physics)

10/22/25, 12:10 PM

WGE: Beam Instrumentation, beam Interaction and AI technology

Contributed Oral Presentation

Fast and precise beam dynamics simulations are essential for particle accelerator design and optimization. Machine learning enables fast end-to-end surrogate models, but these often fail in beamline parameter optimization. We propose a physics-constrained temporal convolutional network (TCN) to predict Twiss parameters along the beamline. The model achieves high-precision 6D to 350×6D mapping,...