Compressive dual-comb spectroscopy in the long-wave infrared region

Area of Science:

• Spectroscopy
• Optical Sensing
• Chemical Analysis

Background:

• Dual-comb spectroscopy (DCS) is a powerful technique for gas analysis.
• Traditional DCS requires high sampling rates, limiting its application in resource-constrained environments.
• Compressive sensing (CS) offers a potential solution for reducing data acquisition requirements.

Purpose of the Study:

• To develop and validate a compressive sensing framework for long-wave infrared (LWIR) dual-comb spectroscopy.
• To assess the framework's performance in spectral reconstruction and trace gas concentration retrieval.
• To demonstrate the framework's applicability for real-time sensing in practical scenarios.

Main Methods:

• Implementation of a compressive sensing framework for LWIR DCS (7.5-11.5 μm).
• Acquisition and processing of undersampled interferograms.
• Spectral reconstruction using CS algorithms.
• Validation using single-species (N2O) and multi-species (CH4, N2O, C2H4) gas mixtures.

Main Results:

• Accurate spectral reconstruction achieved with high compression factors.
• For N2O detection, spectral fidelity preserved up to compression factor 20 (L2 norm residuals < 1.3 × 10^-4).
• Retrieved N2O concentrations maintained >90% accuracy up to compression factor 30.
• In gas mixtures, all species (CH4, N2O, C2H4) retrieved within 10% relative deviation up to compression factor 50.

Conclusions:

• The proposed CS framework enables accurate and efficient LWIR DCS.
• The framework significantly reduces data requirements, allowing for scalable deployment.
• This technology facilitates real-time trace gas sensing in resource-limited applications.