Understanding the Formation and Evolution of Interstellar Ices: A Bayesian Approach

Abstract

Understanding the physical conditions of dark molecular clouds and star forming regions is an inverse problem subject to complicated chemistry that varies non-linearly with time and the physical environment. In this paper we apply a Bayesian approach based on a Markov Chain Monte Carlo (MCMC) method for solving the non-linear inverse problems encountered in astrochemical modelling. We use observations for ice and gas species in dark molecular clouds and a time dependent, gas grain chemical model to infer the values of the physical and chemical parameters that characterize quiescent regions of molecular clouds. We show evidence that in high dimensional problems, MCMC algorithms provide a more efficient and complete solution than more classical strategies. The results of our MCMC method enable us to derive statistical estimates and uncertainties for the physical parameters of interest as a result of the Bayesian treatment.

40 pages, 4 figures, Published in the the Astrophysical Journal

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• Mapping Synthetic Observations to Prestellar Core Models: An Interpretable Machine Learning Approach
• Identifying the most constraining ice observations to infer molecular binding energies
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