Full-spectrum infrared fingerprinting: A transformative AI paradigm for interstellar polycyclic aromatic hydrocarbons

In the era of high-sensitivity infrared (IR) astronomy, traditional manual diagnostics are no longer sufficient to harvest the complex physical insights hidden within interstellar spectra. We introduce a machine learning paradigm that bypasses the limitations of empirical band ratios by treating the complete IR spectrum of polycyclic aromatic hydrocarbons (PAHs) as a high-dimensional fingerprint. Using a random forest classifier trained on ∼ spectra, we achieved a robust F_1 score of ∼0.963 across 12 size and charge categories, maintaining high performance on unseen molecular mixtures. 23000 Interrogating the model's decision-making process reveals that PAH size diagnostics are charge-dependent. Neutral PAHs are traced by modes, while ionized species rely on 6-- C-H 8 C-C morphology; however, the feature remains a versatile tracer across multiple charge states. 12.5 This AI-driven paradigm offers a new route to interpret IR signatures and probe the chemical complexity of the interstellar medium.