Sensitive impulsive stimulated Brillouin spectroscopy by an adaptive noise-suppression Matrix Pencil

Impulsive stimulated Brillouin spectroscopy (ISBS) plays a critical role in investigating mechanical properties thanks to its fast measurement rate. However, traditional Fourier transform-based data processing cannot decipher measured data sensitively because of its incompetence in dealing with low signal-to-noise ratio (SNR) signals caused by a short exposure time and weak signals in a multi-peak spectrum. Here, we propose an adaptive noise-suppression Matrix Pencil method for heterodyne ISBS as an alternative spectral analysis technique, speeding up the measurement regardless of the low SNR and enhancing the sensitivity of multi-component viscoelastic identification. The algorithm maintains accuracy of 0.005% for methanol sound speed even when the SNR drops 33 dB and the exposure time is reduced to 0.4 ms. Moreover, it proves to extract a weak component that accounts for 6 degrees lo from a polymer mixture, which is inaccessible for the traditional method. With its outstanding ability to sensitively decipher weak signals without spectral a priori information and regardless of low SNRs or concentrations, this method offers a fresh perspective for ISBS on fast viscoelasticity measurements and multi-component identifications. (C) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Automated summary

Impulsive stimulated Brillouin spectroscopy (ISBS) is a critical method for fast measurement of mechanical properties. Traditional data processing methods are inefficient in dealing with low signal-to-noise ratio (SNR) signals. In this study, an adaptive noise-suppression Matrix Pencil method is proposed to improve the sensitivity and speed of multi-component viscoelastic identification.