Explaining the Chemical Inventory of Orion KL through Machine Learning

TL;DR

This study applies machine learning regression models to understand the complex chemical inventory of the Orion KL nebula, extending previous work on simpler regions to a more chemically diverse environment.

Contribution

It demonstrates that machine learning models can accurately predict molecular column densities in a complex, high-mass star-forming region, advancing astrochemical modeling capabilities.

Findings

Successfully reproduced molecular column densities in Orion KL

Extended machine learning approaches to complex astrochemical environments

Showed non-linear correlations between different regions' molecular abundances

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).