基于快速光学建模的CERN SPS北区H4与M2束流线大规模优化
Fast optics-based modeling enabling large-scale optimization of the H4 and M2 beamlines in the CERN SPS North Area
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中文总结 AI 辅助
本研究用遗传算法结合Xsuite快速光学模型优化CERN SPS北区H4、M2束流线，经BDSIM模拟与实验验证，显著提升电子/缪子束流性能并降低本底。
中文摘要 AI 辅助
CERN的多用途次级束流线是为国际粒子物理界提供丰富多样物理项目的重要设施，为固定靶实验和测试束流用户提供多种不同粒子种类及束流特性。为实现这种灵活性，需要大量磁元件，这给束流线光学的设计与优化带来重大挑战。本研究采用遗传算法对CERN SPS北区的H4与M2束流线进行优化，旨在证明基于快速光学的跟踪模型（此处为Xsuite）的有效性，以在标准计算硬件上实现大规模多目标优化。该模型通过BDSIM高保真蒙特卡罗模拟进行基准测试与验证，并结合实验测量结果交叉验证。应用于NA64的H4电子配置时，优化后的光学系统使可用电子束流强度提升30%，束流相关本底降低5倍；在M2线中，优化配置使电子束流强度提升近2倍，缪子传输效率提升67%。
英文摘要
The CERN multi-purpose secondary beamlines are invaluable facilities offering a rich and diverse physics program to the international particle physics community, providing many different particle species and beam characteristics to fixed-target experiments and test-beam users. A number of magnetic elements are required to enable such flexibility, posing a significant challenge in the design and optimization of the beamline optics. In this work, we present the optimization of the H4 and M2 beamlines of the CERN SPS North Area using genetic algorithms. The aim of this study is to demonstrate the effectiveness of fast optics-based tracking models (here Xsuite), to enable large-scale, multi-objective optimization on standard computing hardware. The model is benchmarked and validated with high-fidelity Monte Carlo simulations using BDSIM and cross-validation of the results with experimental measurements. The optimized optics, applied to the H4 electron configuration for NA64, achieved a 30% increase in available electron rate and a five-fold reduction in beam-related background. In the M2 line, the optimized configurations yielded a nearly two-fold increase in electron rate and a 67% improvement in muon transmission.
