激光等离子体加速器运行中的物理信息漂移诊断

中文摘要 AI 辅助

激光等离子体加速器（LPA）能维持约$100\\,\mathrm{GV/m}$量级的加速梯度，但其常规运行仍面临困难：电子束指标在一个运行班次内会发生漂移，而根本的物理原因往往在现有诊断手段下不可见。我们将LPA运行建模为一个潜在状态空间模型，其中三个有效的相互作用点变量，即归一化激光振幅$a_0$、归一化等离子体电子密度$\tilde n_e$和残余脉冲啁啾$\mathcal{C}$，通过扩展卡尔曼滤波器从常规电子束观测中推断得出。将潜在状态映射到诊断量的发射模型，在结构上与描述潜在状态在两次发射之间如何演化的转移模型保持分离。这种分离支持两阶段诊断，第一阶段询问哪个潜在变量发生了移动，第二阶段询问是什么导致了它的移动。所实现的发射模型是一个玩具模型，其预期性能与当前设施水平相当，并在相关处采用三维吹出机制依赖关系。我们进行了合成会话以测试检测和归因协议的有效性，发现归因受到激发而非发射次数或诊断分辨率的限制。由于该构建仅需一组物理潜在变量、一个发射模型和一组源自硬件的转移模型，因此它可以迁移到其他易漂移子系统。我们认为整个过程的准确性受限于发射模型而非推断方法。

英文摘要

Laser-plasma accelerators (LPAs) sustain accelerating gradients of order $100\,\mathrm{GV/m}$, but routine operation remains difficult: electron beam metrics drift over an operating shift, and the root physical cause is often invisible to the available diagnostics. We formulate LPA operation as a latent state-space model in which three effective interaction-point variables, the normalized laser amplitude $a_0$, the normalized plasma electron density $\tilde n_e$ and the residual pulse chirp $\mathcal{C}$, are inferred from routine electron beam observations by an extended Kalman filter. The emission model, which maps the latent state to the diagnostics, is kept structurally separate from the {transition} model, which describes how the latent state evolves between shots. The separation supports diagnosis in two stages, one asking which latent variable moved and one asking what moved it. The implemented emission model is a toy model, yielding an expected performance in line with current facilities and using 3D blow-out regime dependencies where relevant. We conduct synthetic sessions to test the effectiveness of the detection and attribution protocols, finding that attribution is limited by excitation rather than by shot count or diagnostic resolution. Because the construction needs only a set of physical latent variables, an emission model and a family of hardware-derived transition models, it transfers to other drift-prone subsystems. We argue that the accuracy of the whole procedure is limited by the emission model rather than by the inference method.

发表机构

• The University of Texas at Austin（德克萨斯大学奥斯汀分校）
• Tau Systems Inc（Tau系统公司）

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