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

Abstract

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 over 23000 spectra, we achieve a robust F1-score of 0.963 across 12 size and charge categories, maintaining high performance on unseen molecular mixtures. Interrogating the model's decision-making process reveals that PAH size diagnostics are charge-dependent. Neutral PAHs are traced by C-H modes, while ionized species rely on 6-8 micron C-C morphology; however, the 12.5micron feature remains a versatile tracer across multiple charge states. This AI-driven paradigm redefines our understanding of IR signatures, providing a transformative lens to probe the chemical complexity of the interstellar medium.