激光唤醒场加速器的帕雷托优化和调整
F Irshad1, C Eberle1, F M Foerster1
1Fakultät für Physik, <a href="https://ror.org/05591te55">Ludwig-Maximilian-Universität München</a>, Am Coulombwall 1, 85748 Garching, Germany.
Physical review letters
|September 6, 2024
概括
多目标贝叶斯优化有效地映射激光唤醒场加速器 (LWFA) 解决方案. 这种方法使精确的能量调整成为可能,并为用户定义的光束参数提出了反向模型,简化了LWFA操作.
科学领域:
- 物理 物理学 物理
- 加速器科学加速器科学
- 计算物理 计算物理
背景情况:
- 优化加速器性能涉及复杂的权衡,特别是在未知的系统.
- 对于像激光唤醒场加速器 (LWFA) 这样的系统来说,平衡多个优化目标是具有挑战性的.
研究的目的:
- 为了证明多目标贝叶斯优化用于绘制LWFA的解决方案空间的有效性.
- 为了实现LWFA精确的能量调整,并为用户指定的光束参数开发一个反向模型.
主要方法:
- 使用多目标贝叶斯优化来探索LWFA的参数空间.
- 采用高斯过程建模来进行前预测,并开发了参数检索的反向模型.
- 同时调整八个参数以调整LWFA能量.
主要成果:
- 确定了广泛的帕雷托最佳解决方案,以一致的激光对光束效率来交易光束能量与电荷.
- 成功证明了LWFA的精确能量调节,从150到400 MeV.
- 开发了一个反向模型,能够为所需的光束特性生成输入参数范围.
结论:
- 多目标贝叶斯优化为LWFA的表征和调整提供了一个样本有效的方法.
- 拟议的反向模型通过启用用户定义的参数设置,大大促进了LWFA操作.
- 这种方法预计将加速LWFA的实际应用和发展.
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