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

TL;DR

This paper introduces a Bayesian MCMC approach to infer physical and chemical parameters of dark molecular clouds from observational data, improving the analysis of complex astrochemical models.

Contribution

It applies a Bayesian Markov Chain Monte Carlo method to solve non-linear inverse problems in astrochemistry, providing more efficient and comprehensive parameter estimation.

Findings

MCMC outperforms classical methods in high-dimensional problems

Provides statistical estimates and uncertainties for physical parameters

Enhances understanding of dark molecular cloud conditions

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.