论文标题
超级弯管中流氓孤子的机器学习分析
Machine learning analysis of rogue solitons in supercontinuum generation
论文作者
论文摘要
SuperContinuum生成是一种高度非线性的过程，它是从注射到光纤异常分散体中的长泵脉冲中发育时表现出不稳定和混乱的。与此制度相关的一个特定特征是在超级弯曲的长波长边缘上长尾的“流氓波”样统计数据，与极端红移的少数“流氓孤子”的产生相关。在这里，我们应用机器学习来分析这些孤子在超局部光谱边缘的特征，并展示监督学习如何训练神经网络以预测这些孤子的峰值功率，持续时间和时间延迟，仅来自超级频谱的峰值延迟，而没有相位。该网络准确地预测了各种场景的孤子特性，从以纯调制不稳定性为主的光谱扩大到具有独特的流氓孤子的近乎八度的超核。
Supercontinuum generation is a highly nonlinear process that exhibits unstable and chaotic characteristics when developing from long pump pulses injected into the anomalous dispersion regime of an optical fiber. A particular feature associated with this regime is the long-tailed "rogue wave"-like statistics of the spectral intensity on the long wavelength edge of the supercontinuum, linked to the generation of a small number of "rogue solitons" with extreme red-shifts. Here, we apply machine learning to analyze the characteristics of these solitons at the edge of the supercontinuum spectrum, and show how supervised learning can train a neural network to predict the peak power, duration, and temporal delay of these solitons from only the supercontinuum spectral intensity without phase information. The network accurately predicts soliton characteristics for a wide range of scenarios, from the onset of spectral broadening dominated by pure modulation instability to near octave-spanning supercontinuum with distinct rogue solitons.
