Electronic Population Reconstruction from Strong-Field-Modified Absorption Spectra with a Convolutional Neural Network

Abstract

We simulate ultrafast electronic transitions in an atom and corresponding absorption line changes with a numerical, few-level model, similar to previous work. In addition, a convolutional neural network (CNN) is employed for the first time to predict electronic state populations based on the simulated modifications of the absorption lines. We utilize a two-level and four-level system, as well as a variety of laser-pulse peak intensities and detunings, to account for different common scenarios of light–matter interaction. As a first step towards the use of CNNs for experimental absorption data in the future, we apply two different noise levels to the simulated input absorption data.