Analysis of Gas Mixtures with Broadband Dual Frequency Comb Spectroscopy and Unsupervised Learning Neural Network

Abstract

Broadband mid‐infrared spectroscopy not only offers supreme sensitivity for the massively parallel detection of trace gases but also presents many challenges. Herein, a new platform combining the advantages of a mid‐infrared dual‐comb spectrometer based on two difference‐frequency generation combs pumped by femtosecond Er‐doped fiber comb oscillators and an unsupervised deep learning neural network consisting of information extraction and information mapping blocks is presented. The scarce data problem, the uncertainties of apparatus, and manual operations intrinsic to multicomponent gas mixture analysis are overcome by coupling an unsupervised leaning approach with a model‐agnostic, physics‐informed data augmentation strategy using simulated data from spectral databases. The system provides reliable simultaneous identification of gas species, concentration retrieval, as well as ambient pressure prediction and eliminates the negative impacts on the measurement, such as model error, baseline fluctuation, and unknown absorbers. Parallel optical detection of 31 different mixtures of 5 gas species over a 2900–3100 cm−1 spectral range with a sub part‐per‐billion sensitivity is demonstrated showing the potential in various applications such as atmospheric monitoring, diagnostics with breath biomarkers, and capturing rapid chemical reaction kinetics.