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You are here: Home / Submitted Papers / 2015 / Understanding the Formation and Evolution of Interstellar Ices: A Bayesian Approach

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 nonlinearly with both 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 nonlinear inverse problems encountered in astrochemical modeling. 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.

Author

Makrymallis, Antonios; Viti, Serena

Journal

Astrophysical Journal

Paper Publication Date

October 2014

Paper Type

Astrostatistics

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