AI Article Synopsis
- Brillouin spectroscopy often experiences low signal-to-noise ratios (SNRs), which can compromise data analysis reliability when SNRs drop below ∼10.
- This study utilizes maximum entropy reconstruction (MER) and wavelet analysis (WA) to enhance the accuracy of Brillouin shift and linewidth measurements, demonstrating effective results even at low SNRs (≥ 1) through Monte-Carlo simulations.
- Denoising techniques were successfully applied to experimental Brillouin spectra of distilled water, allowing for a calculation of sound speed that matched theoretical values within ±1% at an SNR of 1.
Article Abstract
Brillouin spectroscopy can suffer from low signal-to-noise ratios (SNRs). Such low SNRs can render common data analysis protocols unreliable, especially for SNRs below ∼10. In this work we exploit two denoising algorithms, namely maximum entropy reconstruction (MER) and wavelet analysis (WA), to improve the accuracy and precision in determination of Brillouin shifts and linewidth. Algorithm performance is quantified using Monte-Carlo simulations and benchmarked against the Cramér-Rao lower bound. Superior estimation results are demonstrated even at low SNRs (≥ 1). Denoising is furthermore applied to experimental Brillouin spectra of distilled water at room temperature, allowing the speed of sound in water to be extracted. Experimental and theoretical values were found to be consistent to within ±1% at unity SNR.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7041457 | PMC |
| http://dx.doi.org/10.1364/BOE.380798 | DOI Listing |
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