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A Bayesian approach to the study of astrochemistry

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RNTW01 - Rich and Nonlinear Tomography (RNT) in Radar, Astronomy and Geophysics

To understand grain-surface chemistry in the interstellar medium, one must be able to determine the reaction rate parameters of reactions of interest. Typically, a Bayesian inference approach is employed to estimate these parameters. However due to the absence of enough sufficiently constraining data, this is often not sufficient. We will consider several different approaches that can be taken to address this problem, ranging from considerations of the reaction rate network to employing knowledge of the physics to reduce the dimensionality of the parameter space. Building on this, we use the Massive Optimised Parameter Estimation and Data (MOPED) compression algorithm to determine what additional data needs to be collected to improve our estimates of the data.

This talk is part of the Isaac Newton Institute Seminar Series series.

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