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• W4281286365 abstract "There exists much uncertainty surrounding interstellar grain-surface chemistry. One of the major reaction mechanisms is grain-surface diffusion for which the the binding energy parameter for each species needs to be known. However, these values vary significantly across the literature which can lead to debate as to whether or not a particular reaction takes place via diffusion. In this work we employ Bayesian inference to use available ice abundances to estimate the reaction rates of the reactions in a chemical network that produces glycine. Using this we estimate the binding energy of a variety of important species in the network, by assuming that the reactions take place via diffusion. We use our understanding of the diffusion mechanism to reduce the dimensionality of the inference problem from 49 to 14, by demonstrating that reactions can be separated into classes. This dimensionality reduction makes the problem computationally feasible. A neural network statistical emulator is used to also help accelerate the Bayesian inference process substantially. The binding energies of most of the diffusive species of interest are found to match some of the disparate literature values, with the exceptions of atomic and diatomic hydrogen. The discrepancies with these two species are related to limitations of the physical and chemical model. However, the use of a dummy reaction of the form H + X -> HX is found to somewhat reduce the discrepancy with the binding energy of atomic hydrogen. Using the inferred binding energies in the full gas-grain version of UCLCHEM results in almost all the molecular abundances being recovered." @default.
• W4281286365 created "2022-05-24" @default.
• W4281286365 creator A5059888794 @default.
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• W4281286365 date "2022-05-01" @default.
• W4281286365 modified "2023-10-16" @default.
• W4281286365 title "Using Statistical Emulation and Knowledge of Grain-surface Diffusion for Bayesian Inference of Reaction Rate Parameters: An Application to a Glycine Network" @default.
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• W4281286365 doi "https://doi.org/10.3847/1538-4357/ac6606" @default.
• W4281286365 hasPublicationYear "2022" @default.
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