Explaining the Chemical Inventory of Orion KL through Machine Learning

Abstract

The interplay of the chemistry and physics that exists within astrochemically relevant sources can only be fully appreciated if we can gain a holistic understanding of their chemical inventories. Previous work by Lee et al. (2021) demonstrated the capabilities of simple regression models to reproduce the abundances of the chemical inventory of the Taurus Molecular Cloud 1 (TMC-1), as well as provide abundance predictions for new candidate molecules. It remains to be seen, however, to what degree TMC-1 is a ``unicorn'' in astrochemistry, where the simplicity of its chemistry and physics readily facilitates characterization with simple machine learning models. Here we present an extension in chemical complexity to a heavily studied high-mass star forming region: the Orion Kleinmann-Low (Orion KL) nebula. Unlike TMC-1, Orion KL is composed of several structurally distinct environments that differ chemically and kinematically, wherein the column densities of molecules between these components can have non-linear correlations that cause the unexpected appearance or even lack of likely species in various environments. This proof-of-concept study used similar regression models sampled by Lee et al. (2021) to accurately reproduce the column densities from the XCLASS fitting program presented in Crockett et al. (2014).Comment: 14 pages; 6 figures, 1 table in the main text. 0 figures, 1 table in the appendix. Accepted for publication in The Astrophysical Journal. Molecular dataset for machine learning can be found in the Zenodo repository here: https://zenodo.org/record/767560