Deep neural networks for high harmonic spectroscopy in solids

Abstract

Neural networks are a prominent tool for identifying and modeling complex patterns, which are otherwise hard to detect and analyse. While machine learning and neural networks have been finding applications across many areas of science and technology, their use in decoding ultrafast dynamics of quantum systems driven by strong laser fields has been limited so far. Here we use deep neural networks to analyze spectra of highly nonlinear optical response of a crystal to intense few-cycle laser pulses. We construct a deep neural network that can efficiently utilize such spectra to resolve both the complex spectral phase of ultrashort laser pulses and simultaneously reconstruct the band structure of the crystal. Our results offer a new tool for attosecond spectroscopy of quantum dynamics and also open a route to developing all-solid-state devices for complete characterization of few-cycle pulses, including their nonlinear chirp and the carrier envelope phase.