Using machine learning to predict dinitrogen molecule density by analyzing transient-dependent nonlinear pulse propagation in an air-filled hollow core photonic crystal fiber

Abstract

Pulse propagation in air-filled hollow core photonic crystal fibers has been well investigated within the last decade to generate nonlinear phenomena such as pulse compression, frequency conversion,
supercontinuum generation, among others, in a highly reliable and reproducible manner.
In this work, we extend the analysis to take into account the recently evidenced pulse-width dependency of the nonlinear refraction index of air and the effects of the molecular composition of air. Our study focuses on the dispersive wave and supercontinuum generation, showing distinct effects on the spectrum due to the pulse-widths dependency along the propagation path. Based on these results we propose a deep learning algorithm that can predict the dinitrogen molecule particle-density of an air sample from different atmospheric conditions.