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首页> 外文期刊>The journal of physical chemistry, A. Molecules, spectroscopy, kinetics, environment, & general theory >Evolutionary Algorithm Optimization of Zeeman Deceleration: Is It Worthwhile for Longer Decelerators?
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Evolutionary Algorithm Optimization of Zeeman Deceleration: Is It Worthwhile for Longer Decelerators?

机译：Zeeman减速的进化算法优化：较长的减速器是值得的吗？

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摘要

In Zeeman deceleration, only a small subset of low-field seeking particles in the incoming beam possess initial velocities and positions that place them within the phase-space acceptance of the device. In order to maximize the number of particles that are successfully decelerated to a selected final velocity, we seek to optimize the phase-space acceptance of the decelerator. Three-dimensional particle trajectory simulations are employed to investigate the potential benefits of using a covariance matrix adaptation evolutionary strategy (CMA-ES) optimization method for decelerators longer than 12 stages and for decelerating species other than H atoms. In all scenarios considered, the evolutionary algorithm optimized sequences yield vastly more particles within the target velocity range. This is particularly evident in scenarios where standard sequences are known to perform poorly; simulations show that CMA-ES optimization of a standard sequence decelerating H atoms from an initial velocity of 500 ms(-1) down to a final velocity of 200 ms(-1) in a 24-stage decelerator produces a considerable 5921% (or 60-fold) increase in the number of successfully decelerated particles. Particle losses that occur with standard pulse sequences for example, arising from the coupling of longitudinal and transverse motion are overcome in the CMA-ES optimization process as the passage of all particles through the decelerator is explicitly considered and focusing effects are accounted for in the optimization process.
机译：在塞曼减速时，进入光束中仅仅在进入光束中寻找颗粒的小子集具有将它们放置在装置的相位空间接受范围内的初始速度和位置。为了最大化成功减速到所选最终速度的粒子的数量，我们寻求优化减速器的相位空间接受。采用三维粒子轨迹模拟来研究利用协方差矩阵适应进化策略（CMA-ES）优化方法的潜在益处，用于减速器长于12阶段，用于减速除H原子以外的逐渐减速。在考虑所有场景中，进化算法优化序列在目标速度范围内产生更多更多粒子。这在已知标准序列执行不良的情况下尤其明显;模拟表明，在24-阶段减速剂中，将标准序列从500ms（-1）的初始速度下降到最终速度的标准序列的CMA-ES优化在24级减速剂中产生相当大的5921％（或60倍）成功减速颗粒的数量增加。例如，在CMA-ES优化过程中克服了标准脉冲序列的用标准脉冲序列发生的粒度损失，因为所有粒子通过减速器的通过明确地考虑并考虑聚焦效应，在优化中被占据了聚焦效应过程。

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