Deep neural networks for high harmonic spectroscopy in solids

TL;DR

This paper demonstrates that deep neural networks can accurately analyze noisy high harmonic spectra from solids, enabling detailed characterization of ultrafast quantum dynamics and pulse properties.

Contribution

It introduces a neural network approach for decoding complex, noisy spectra of solids driven by strong laser fields, facilitating ultrafast spectroscopy and pulse characterization.

Findings

Neural networks accurately recover band structures from noisy spectra

The approach works for complex 2D materials like gapped graphene

Method enables characterization of pulse phase and chirp despite noise

Abstract

Neural networks are a prominent tool for identifying and modeling complex patterns, which are otherwise hard to detect and analyze. 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 simulated noisy spectra of highly nonlinear optical response of a 2-dimensional gapped graphene crystal to intense few-cycle laser pulses. We show that a computationally simple 1-dimensional system provides a useful "nursery school" for our neural network, allowing it to be easily retrained to treat more complex systems, recovering the band structure and spectral phases of the incident few-cycle pulse with high accuracy, in spite of significant amplitude noise and phase jitter. Our results both offer a new tool for attosecond spectroscopy of quantum dynamics in solids 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.