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• W4285010672 abstract "The terahertz region presents promising opportunities for quantitative gas sensing in practical environments thanks to its access to molecular fingerprints and immunity to particulate scattering. With Terahertz time-domain spectroscopy (THz-TDS), molecular absorption and dispersion spectra can be rendered using femtosecond-laser-based interferometric system. However, procurement of accurate spectroscopic parameters in this uncharted spectral region and quantitative reduction of the broadband data remain challenging. We propose and validate two end-to-end machine learning models based on Gaussian process regression (GPR) for efficient THz-TDS sensing in both time- and frequency-domain (TD and FD). Quantitative CO sensing is accomplished for proof-of-concept demonstration of the method. Both TD- and FD-GPR models demonstrate accurate predictions in the presence of measurement noise and ambient interference, and the root-mean-square errors are below 0.6 % with both simulated and experimental data. It is concluded that while the FD model benefits from immunity to dispersion distortions, the TD model noses out for its baseline-free nature and better elimination of temporally introduced noises. This work presents the first experimental demonstration of THz-TDS for quantitative gas sensing exploiting both TD and FD signals, showing promising prospects for real-time, multi-species, multi-parameter sensing under challenging scenarios such as chemically reactive or open-path atmospheric environments. • Terahertz spectroscopy has prospects for gas sensing in challenging environments. • Absorption and dispersion spectra can be measured with interferometric THz system. • Gaussian process regression efficiently interprets THz time-/frequency-domain data. • Direct inference of gas properties from temporal profile is successful and accurate. • Time-domain processing benefits from better immunity to power fluctuation and noise." @default.
• W4285010672 created "2022-07-12" @default.
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• W4285010672 date "2022-10-01" @default.
• W4285010672 modified "2023-10-14" @default.
• W4285010672 title "Efficient terahertz absorption gas sensor with Gaussian process regression in time- and frequency-domain" @default.
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• W4285010672 doi "https://doi.org/10.1016/j.snb.2022.132349" @default.
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