AI 中文总结
该研究针对驱动太赫兹超辐射波导自由电子激光的6MeV ORGAD加速器，采用机器学习贝叶斯优化方法，经25次模拟迭代得到最优电子束传输设计，以实现超辐射自由电子激光的最佳运行。
AI 中文摘要
我们提出一种使用机器学习（ML）库的优化程序，用于优化电子束传输，以实现最大束团压缩和束团型超辐射自由电子激光的最佳运行。这一点在阿里尔大学6MeV ORGAD加速器的参数上得到了例证，该加速器驱动太赫兹超辐射波导自由电子激光。对于超辐射发射（与电子数的平方成正比），波荡器处的束团持续时间σ_t应短于辐射的光学周期（2π/ω）。此外，束流传输优化必须将束流的横向尺寸限制在能够进入波荡器波导的范围内。机器学习贝叶斯优化的变量是束流线上的射频参数以及线圈和四极磁体的电流。束流尺寸和持续时间由完整的三维GPT模拟提供，这些模拟由机器学习的探索与利用算法自动驱动。25次模拟迭代足以获得最优的束流传输设计。
英文摘要
We present an optimization procedure using machine learning (ML) libraries for optimization of electron beam transport for maximal bunch compression and optimal operation of a bunched-beam Superradiant FEL. This is exemplified for the parameters of the 6MeV ORGAD Accelerator at Ariel University that is driving a THz Superradiant waveguide FEL. For superradiant emission (proportionally to the number of electrons squared), the bunch duration $σ_t$ at the undulator should be shorter than the optical period (2$π/ω$) of the radiation. Also, the beam transport optimization must confine the transverse dimensions of the beam to enter the undulator waveguide. The variables of the ML Bayesian optimization are the RF parameters and the currents of the coils and quads along the beamline. The beam dimensions and duration are provided from full 3D GPT simulations that are automatically driven by the ML exploration and exploitation algorithms. Twenty-five simulation iterations sufficed to arrive to an optimal beam transport design.
Comments13 pages, 9 figures
