Bayesian Inference of the Rates of Surface Reactions in Icy Mantles

Authors: Holdship, J.; Jeffrey, N.; Makrymallis, A.; Viti, S.; Yates, J.
Arxiv: https://arxiv.org/abs/1809.06245
DOI: https://doi.org/10.3847/1538-4357/aae1fa

Grain surface chemistry and its treatment in gas-grain chemical models is an area of large uncertainty. While laboratory experiments are making progress, there is still much that is unknown about grain surface chemistry. Further, the results and parameters produced by experiments are often not easily translated to the rate equation approach most commonly used in astrochemical modeling. It is possible that statistical methods can reduce the uncertainty in grain surface chemical networks. In this work, a simple model of grain surface chemistry in a molecular cloud is developed and a Bayesian inference of the reactions rates is performed through Markov Chain Monte Carlo sampling. Using observational data of the solid state abundances of major chemical species in molecular clouds, the posterior distributions for the rates of seven reactions producing CO, CO2, CH3OH, and H2O are calculated in a form that is suitable for rate equation models. This represents a vital first step in the development of a method to infer reaction rates from observations of chemical abundances in astrophysical environments.