用扩散模型将加速器传感器信号转为粒子束二维相空间图
Adaptive conditional latent diffusion maps beam loss to 2D phase space projections
- 用条件扩散模型学习多传感器波形与6D相空间投影的映射关系
- 在LANSCE线性质子加速器上成功还原出高精度2D相空间分布
- 让普通传感器变身高维束流诊断工具，适用于任何加速器
粒子加速器中普遍使用束流损失监测仪（BLM）和束流电流监测仪（BCM），这些非侵入式设备可提供高水平的束流测量，但无法揭示6D（x,y,z,px,py,pz）相空间分布或动力学细节。本文表明，生成式条件隐变量扩散模型能够学习复杂模式，将沿加速器布置的数十个BLM或BCM的波形，映射为带电粒子束6D相空间密度的详细二维投影。该方法具有转换性，可在任意粒子加速器上应用，使简单非侵入式设备具备高分辨率相空间诊断能力。我们通过千米级长的LANSCE线性质子加速器中的多粒子模拟验证了这一概念。
原文摘要 · Abstract (English)
Beam loss (BLM) and beam current monitors (BCM) are ubiquitous at particle accelerator around the world. These simple devices provide non-invasive high level beam measurements, but give no insight into the detailed 6D (x,y,z,px,py,pz) beam phase space distributions or dynamics. We show that generative conditional latent diffusion models can learn intricate patterns to map waveforms of tens of BLMs or BCMs along an accelerator to detailed 2D projections of a charged particle beam's 6D phase space density. This transformational method can be used at any particle accelerator to transform simple non-invasive devices into detailed beam phase space diagnostics. We demonstrate this concept via multi-particle simulations of the high intensity beam in the kilometer-long LANSCE linear proton accelerator.
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