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astro-ph.GA2026
A machine-learning-driven dataset of 144,111 polycyclic aromatic hydrocarbon infrared spectra for interpreting aromatic infrared bands
Xinghong Mai, Zu-Jia Lu, Zhao Wang
The interpretation of interstellar aromatic infrared bands (AIBs) relies on reference spectral libraries, but the chemical space covered by existing databases remains incomplete re…
astro-ph.GA2026
Graph Neural Network Prediction of Infrared Spectra of Interstellar Polycyclic Aromatic Hydrocarbons
Guoqing Tang, Jiang He, Zhao Wang +1
Polycyclic aromatic hydrocarbons (PAHs) are recognized as the primary contributors to the aromatic infrared bands (AIBs) widely observed in space. However, analyzing these AIBs rem…
astro-ph.GA2026
Full-spectrum infrared fingerprinting: A transformative AI paradigm for interstellar polycyclic aromatic hydrocarbons
Zhao Wang
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…