Robust Identification and Recovery of Obscured CO₂ Absorption Signals Using Deep Machine Learning

We present the efficacy of deep learning (DL) in identifying and recovering the CO2 absorption line from a noisy spectrum. Following a simulation-based assessment of the DL method’s capabilities, it was applied in an experiment utilizing a lock-in amplifier and mechanical chopper as a phase-sensitive detection unit. The results demonstrate that the DL method is fully qualified to replace traditional noise-reduction systems, simplifying spectroscopy while ensuring reliability and intelligence.