Understanding molecular ratios in the carbon and oxygen poor outer Milky Way with interpretable machine learning

TL;DR

This study employs interpretable machine learning techniques to analyze molecular line ratios in the outer Milky Way, revealing key environmental parameters influencing molecular chemistry and identifying effective probes for initial carbon and oxygen abundances.

Contribution

The paper introduces the use of SHAP and UMAP methods to interpret molecular line ratios in astrochemical models, providing new insights into the chemistry of low-metallicity regions.

Findings

Temperature and density are primary factors affecting line ratios.

CN/HCN and HNC/HCN are sensitive to initial carbon abundance.

CS/SO ratio is sensitive to oxygen abundance.

Abstract

Context. The outer Milky Way has a lower metallicity than our solar neighbourhood, but still many molecules are detected in the region. Molecular line ratios can serve as probes to better understand the chemistry and physics in these regions. Aims. We use interpretable machine learning to study 9 different molecular ratios, helping us understand the forward connection between the physics of these environments and the carbon and oxygen chemistries. Methods. Using a large grid of astrochemical models generated using UCLCHEM, we study the properties of molecular clouds of low oxygen and carbon initial abundance. We first try to understand the line ratios using a classical analysis. We then move on to using interpretable machine learning, namely Shapley Additive Explanations (SHAP), to understand the higher order dependencies of the ratios over the entire parameter grid. Lastly we use the Uniform Manifold Approximation and Projection technique (UMAP) as a reduction method to create intuitive groupings of models. Results. We find that the parameter space is well covered by the line ratios, allowing us to investigate all input parameters. SHAP analysis shows that the temperature and density are the most important features, but the carbon and oxygen abundances are important in parts of the parameter space. Lastly, we find that we can group different types of ratios using UMAP. Conclusions. We show the chosen ratios are mostly sensitive to changes in the carbon initial abundance, together with the temperature and density. Especially the CN/HCN and HNC/HCN ratio are shown to be sensitive to the initial carbon abundance, making them excellent probes for this parameter. Out of the ratios, only CS/SO shows a sensitivity to the oxygen abundance.