Exploiting Network Topology for Accelerated Bayesian Inference of Grain Surface Reaction Networks

Abstract

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.

References in corpus (17)

• Observations of the Icy Universe
• A three-phase chemical model of hot cores: the formation of glycine
• Data analysis recipes: Using Markov Chain Monte Carlo
• H-atom addition and abstraction reactions in mixed CO, H2CO and CH3OH ices: an extended view on complex organic molecule formation
• Modeling Sulfur Depletion in Interstellar Clouds
• Atom addition reactions in interstellar ice analogues
• Astrochemistry of dust, ice and gas: introduction and overview
• UCLCHEM: A Gas-Grain Chemical Code
• A new study of an old sink of sulfur in hot molecular cores: the sulfur residue
• A new look at sulphur chemistry in hot cores and corinos
• Incorporating Astrochemistry into Molecular Line Modelling via Emulation
• The impact of freeze-out on collapsing molecular clouds
• Properties of the Affine Invariant Ensemble Sampler in high dimensions
• Complexity reduction of astrochemical networks
• Bayesian Inference of Reaction Rates in Icy Mantles
• Chemical network reduction in protoplanetary disks
• Understanding the Formation and Evolution of Interstellar Ices: A Bayesian Approach

Cited by in corpus (3)

• Reducing the complexity of chemical networks via interpretable autoencoders
• Identifying the most constraining ice observations to infer molecular binding energies
• Using Statistical Emulation and Knowledge of Grain-Surface Diffusion for Bayesian Inference of Reaction Rate Parameters: An Application to a Glycine Network