Removal of cross-phase modulation artifacts in ultrafast pump–probe dynamics by deep learning

A Bresci,F Scotognella,C M Valensise,M Guizzardi,F Marangi,G Cerullo,D Polli

doi:10.1063/5.0057404

Abstract

Pump–probe spectroscopy is a gold standard technique to investigate ultrafast electronic dynamics of material systems. Pulsed laser sources employed to pump and probe samples feature typically high peak power, which may give rise to coherent artifacts under a wide range of experimental conditions. Among those, the Cross-Phase Modulation (XPM) artifact has gathered particular attention as it produces particularly high signal distortions, in some cases hiding a relevant portion of the dynamics of interest. Here, we present a novel approach for the removal of XPM coherent artifacts in ultrafast pump–probe spectroscopy, based on deep learning. We developed XPMnet, a convolutional neural network able to reconstruct electronic relaxation dynamics otherwise embedded in artifact distortions, thus enabling the retrieval of fundamental information to characterize the material system under investigation. We validated XPMnet on Indium Tin Oxide (ITO), a heavily doped semiconductor displaying a plasmon resonance in the near-infrared, which is a key material for the development of infrared plasmonic devices. Pump–probe measurements of ITO show strong XPM artifacts that overwhelm the electronic cooling dynamics of interest due to the low optical density of the material at near-infrared photon energies. XPMnet retrieved ITO electronic dynamics in excellent agreement with expected outcomes in terms of material-specific time constants. This artificial intelligence method constitutes a powerful solution for XPM artifact removal, providing high accuracy and short execution time. We believe that this model could be integrated in real time in pump–probe setups to increase the amount of information one can derive from ultrafast spectroscopy measurements.

Highlights

Ultrafast pump–probe spectroscopy has proven to be a powerful technique to study out-of-equilibrium phenomena, being applicable over a broad range of photon energies from THz to x rays.1 In pump–probe, a medium is first excited with a short pump pulse and the photoinduced dynamics is probed by a time-delayed broadband probe pulse
We proposed a novel Artificial Intelligence (AI)-driven method—XPMnet—for the removal of XPM Coherent Artifacts (CAs) in ultrafast pump–probe spectroscopy signals, providing a powerful tool to reveal electronic relaxation dynamics otherwise highly distorted in a wide variety of common experimental conditions
The Deep Learning (DL) model here presented performs with high accuracy (R2 = 0.99, mean squared error (MSE) = 5 ⋅ 10−5) in a very short execution time (3 ⋅ 10−2 s): it could be integrated into pump–probe spectroscopy setups to process data in real time for artifact removal

Summary

Introduction

Ultrafast pump–probe spectroscopy has proven to be a powerful technique to study out-of-equilibrium phenomena, being applicable over a broad range of photon energies from THz to x rays. In pump–probe, a medium is first excited with a short pump pulse and the photoinduced dynamics is probed by a time-delayed broadband probe pulse. The excitation pulses commonly employed are shorter than or close to 100 fs, which leads to peak intensities higher than 1 GW/cm2 Such a condition may promote the generation of several Coherent Artifacts (CAs) of considerable intensity that can completely or partially distort the first hundreds of femtoseconds of relaxation dynamics, causing loss of information about early electronic processes under investigation. XPM was first reported in 1986 by Alfano et al.: it originates from the redistribution of the spectral components of the probe pulse induced by the Kerr effect, namely, a change in the medium refractive index n caused by an intense pump pulse with intensity Ipu(t), according to n(t) = n0 + n2 ⋅ Ipu(t) Such a rapid refractive index change modulates the phase shift experienced by the probe pulse and causes time-dependent shifts of its spectrum, which give rise to positive/negative differential transmission (ΔT/T) signals at specific probe wavelengths. XPM-related distortions are unavoidable when employing glass substrates and when samples under investigation feature a low optical density in the range of the pump pulse

Methods

Results

Conclusion