Bayesian Inference of Reaction Rates in Icy Mantles

TL;DR

This paper develops a Bayesian inference framework using MCMC sampling to estimate grain surface reaction rates in molecular clouds, reducing uncertainties in astrochemical models based on observational data.

Contribution

It introduces a novel Bayesian approach to infer reaction rates from observational data, bridging laboratory results and rate equation models in astrochemistry.

Findings

Posterior distributions for seven reaction rates were successfully derived.

The method provides rate estimates compatible with observational solid-state abundances.

This approach offers a new pathway to constrain grain surface chemistry in astrophysical environments.

Abstract

Grain surface chemistry and its treatment in gas-grain chemical models is an area of large uncertainty. Whilst laboratory experiments are making progress, there is still much that is unknown about grain surface chemistry. Further, the results and parameters produced by experiment are often not easily translated to the rate equation approach most commonly used in astrochemical modelling. 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 MCMC 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, CO$_2$, CH$_3$OH and H$_2$O 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.