一个有条件的潜伏自回归循环模型,用于生成和预测粒子加速器中的光束动态
Mahindra Rautela1, Alan Williams2,3, Alexander Scheinker4
1Applied Electrodynamics Group (AOT-AE), Los Alamos National Laboratory, Los Alamos, NM, 87547, USA. mrautela@lanl.gov.
Scientific reports
|August 5, 2024
概括
我们开发了一个深度学习模型,即条件潜伏自回归循环模型 (CLARM),以了解加速器中的带电粒子束动力学. 这个框架显示出产生预测和预测粒子行为的前景.
科学领域:
- 物理 物理学 物理
- 加速器物理学的物理学
- 计算物理 计算物理
背景情况:
- 粒子加速器需要精确控制强烈的带电粒子束.
- 由于测量限制,复杂的模拟和系统不确定性,光束诊断具有挑战性.
研究的目的:
- 引入一种新的无监督深度学习框架,用于模拟带电粒子束动态.
- 通过学习时空行为来解决光束诊断方面的挑战.
主要方法:
- 一个由两个步骤组成的无监督深度学习框架,名为条件潜伏自行回归循环模型 (CLARM).
- 使用条件变量自编码器用于相位变换和长短期内存网络用于时间动态.
- 采用自回归式学习来捕获顺序数据.
主要成果:
- 克拉姆成功地学习了带电粒子的时空动态.
- 该模型可以在加速器模块中生成粒子行为投影.
- 从过去的状态来看,CLARM可以准确地预测未来的粒子状态.
结论:
- 拟议的CLARM框架为粒子加速器中先进的光束诊断提供了一个有希望的方法.
- 深度学习为理解和预测复杂的加速器系统动态提供了强大的工具.
- 克拉姆的生成和预测能力需要进一步研究现实世界的应用.
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