Sensitivity Analysis of Aromatic Chemistry to Gas-Phase Kinetics in a Dark Molecular Cloud Model

TL;DR

This study applies sensitivity analysis methods to a dark cloud astrochemical model to identify reactions that significantly influence aromatic molecule formation, emphasizing the importance of accurate rate constants for key reactions.

Contribution

It introduces a comprehensive sensitivity analysis approach to astrochemical models, pinpointing reactions critical for aromatic molecule synthesis in interstellar environments.

Findings

Reactions involving small reactive species greatly impact the chemical network.

Cyanonaphthalene sensitivity is linked to ring formation and aromatic growth reactions.

Prioritizing rate constant constraints for key reactions improves model accuracy.

Abstract

The increasingly large number of complex organic molecules detected in the interstellar medium necessitates robust kinetic models that can be relied upon for investigating the involved chemical processes. Such models require rate constants for each of the thousands of reactions; the values of these are often estimated or extrapolated, leading to large uncertainties that are rarely quantified. We have performed a global Monte Carlo and a more local one-at-a-time sensitivity analysis on the gas-phase rate coefficients in a 3-phase dark cloud model. Time-dependent sensitivities have been calculated using four metrics to determine key reactions for the overall network as well as for the cyanonaphthalene molecule in particular, an important interstellar species that is severely under-produced by current models. All four metrics find that reactions involving small, reactive species that initiate hydrocarbon growth have large effects on the overall network. Cyanonaphthalene is most sensitive to a number of these reactions as well as ring-formation of the phenyl cation (C6H5+) and aromatic growth from benzene to naphthalene. Future efforts should prioritize constraining rate coefficients of key reactions and expanding the network surrounding these processes. These results highlight the strength of sensitivity analysis techniques to identify critical processes in complex chemical networks, such as those often used in astrochemical modeling.