Exploiting Network Topology for Accelerated Bayesian Inference of Grain
  Surface Reaction Networks
  In the study of grain-surface chemistry in the interstellar medium, there
exists much uncertainty regarding the reaction mechanisms with few constraints
on the abundances of grain-surface molecules. Bayesian inference can be
performed to determine the likely reaction rates. In this work, we consider
methods for reducing the computational expense of performing Bayesian inference
on a reaction network by looking at the geometry of the network. Two methods of
exploiting the topology of the reaction network are presented. One involves
reducing a reaction network to just the reaction chains with constraints on
them. After this, new constraints are added to the reaction network and it is
shown that one can separate this new reaction network into sub-networks. The
fact that networks can be separated into sub-networks is particularly important
for the reaction networks of interstellar complex organic molecules, whose
surface reaction networks may have hundreds of reactions. Both methods allow
the maximum-posterior reaction rate to be recovered with minimal bias.
