Laser Wakefield Accelerator modelling with Variational Neural Networks
Laser Wakefield Accelerator modelling with Variational Neural Networks
复制标题
使用变分神经网络的激光韦克场加速器建模
DOI：
10.1017/hpl.2022.47
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发表时间：
2023
影响因子：
4.8
通讯作者：
Streeter M
中科院分区：
文献类型：
--
作者：
Streeter M
建立了一个机器学习模型来预测GeV级激光韦克菲尔德加速器产生的电子光谱。该模型由变分卷积神经网络构建，该网络将二次激光和等离子体诊断的结果映射到生成的电子光谱。训练网络的集合用于预测电子光谱，并提供该预测的不确定性的估计。预计这种方法将是有用的推断电子光谱之前，经历任何过程，可以改变或破坏束。此外，该模型提供了洞察电子束特性的缩放由于随机波动的激光能量和等离子体电子密度。
A machine learning model was created to predict the electron spectrum generated by a GeV-class laser wakefield accelerator. The model was constructed from variational convolutional neural networks, which mapped the results of secondary laser and plasma diagnostics to the generated electron spectrum. An ensemble of trained networks was used to predict the electron spectrum and to provide an estimation of the uncertainty of that prediction. It is anticipated that this approach will be useful for inferring the electron spectrum prior to undergoing any process that can alter or destroy the beam. In addition, the model provides insight into the scaling of electron beam properties due to stochastic fluctuations in the laser energy and plasma electron density.