Decoding Starlight: Machine Learning Reveals PAH Secrets
![The ratio of emission intensities at 11.2 and 3.3 micrometers-a proxy for molecular complexity-correlates with the number of carbon atoms in polycyclic aromatic hydrocarbons (PAHs), as demonstrated by analysis of a dataset of 15,022 neutral PAHs-including a subset of 81 identified by Maragkoudakis et al. (2020)-and refined using a 6 eV cascade model, yielding a robust fit-indicated by [latex]R^{2}[/latex] values-that suggests a predictable relationship between molecular size and infrared spectral features.](https://arxiv.org/html/2602.12531v1/x1.png)
A new machine learning technique accurately identifies the size and charge of polycyclic aromatic hydrocarbons in space by analyzing their infrared light signatures.
![The ratio of emission intensities at 11.2 and 3.3 micrometers-a proxy for molecular complexity-correlates with the number of carbon atoms in polycyclic aromatic hydrocarbons (PAHs), as demonstrated by analysis of a dataset of 15,022 neutral PAHs-including a subset of 81 identified by Maragkoudakis et al. (2020)-and refined using a 6 eV cascade model, yielding a robust fit-indicated by [latex]R^{2}[/latex] values-that suggests a predictable relationship between molecular size and infrared spectral features.](https://arxiv.org/html/2602.12531v1/x1.png)
A new machine learning technique accurately identifies the size and charge of polycyclic aromatic hydrocarbons in space by analyzing their infrared light signatures.

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