Full-Spectrum Machine Learning Diagnostics for Interstellar PAHs
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866913050116751360 |
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| author | Wang, Zhao |
| author_facet | Wang, Zhao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_12531 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Full-Spectrum Machine Learning Diagnostics for Interstellar PAHs Wang, Zhao Astrophysics of Galaxies Instrumentation and Methods for Astrophysics Solar and Stellar Astrophysics 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. |
| title | Full-Spectrum Machine Learning Diagnostics for Interstellar PAHs |
| topic | Astrophysics of Galaxies Instrumentation and Methods for Astrophysics Solar and Stellar Astrophysics |
| url | https://arxiv.org/abs/2602.12531 |