论文标题
用合成汉密尔顿矩阵训练的深神经网络分析非线性照片电离光谱的观点
Perspectives for analyzing non-linear photo ionization spectra with deep neural networks trained with synthetic Hamilton matrices
论文作者
论文摘要
我们已经构建了深层神经网络，可以绘制从嘈杂脉冲获得的波动光电光谱到无噪声脉冲的光谱。该网络是从嘈杂的脉冲中与随机汉密尔顿矩阵结合使用的光谱训练的，代表可能存在但不一定存在的系统。在[Giri等人，物理学。莱特牧师。 124,113201（2020）]我们进行了波动光谱的纯化，将它们映射到了来自傅立叶限制的高斯脉冲的光谱。在这里，我们研究了这种基于神经网络的地图的性能，以预测双脉冲，刺耳的脉冲，甚至是噪音脉冲产生的波动光谱中的脉冲脉冲的脉冲。其次，我们证明，除了对波动的双脉冲光谱的净化，人们可以估计基础双脉冲的时间延迟，这是Sase Fels的单发光谱的吸引人特征。我们用共振的两光子离子化（一种非线性过程）证明了我们的方法，对激光脉冲的细节敏感。
We have constructed deep neural networks, which can map fluctuating photo-electron spectra obtained from noisy pulses to spectra from noise-free pulses. The network is trained on spectra from noisy pulses in combination with random Hamilton matrices, representing systems which could exist but do not necessarily exist. In [Giri et al., Phys. Rev. Lett. 124,113201 (2020)] we performed a purification of fluctuating spectra, that is mapping them to those from Fourier-limited Gaussian pulses. Here, we investigate the performance of such neural-network-based maps for predicting spectra of double pulses, pulses with a chirp and even partially-coherent pulses pulses from fluctuating spectra generated by noisy pulses. Secondly, we demonstrate that along with a purification of a fluctuating double-pulse spectrum, one can estimate the time-delay of the underlying double pulse, an attractive feature for single-shot spectra from SASE FELs. We demonstrate our approach with resonant two-photon ionization, a non-linear process, sensitive to details of the laser pulse.
