UNDERSTANDING THE FORMATION AND EVOLUTION OF INTERSTELLAR ICES: A BAYESIAN APPROACH

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) m...

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Published inThe Astrophysical journal Vol. 794; no. 1; pp. 45 - 12
Main Authors Makrymallis, Antonios, Viti, Serena
Format Journal Article
LanguageEnglish
Published United States 10.10.2014
Subjects
ALGORITHMS
ASTROPHYSICS, COSMOLOGY AND ASTRONOMY
Bayesian analysis
DATA ANALYSIS
Evolution
ICE
Inverse problems
MARKOV PROCESS
Mathematical models
MATHEMATICAL SOLUTIONS
Molecular clouds
MONTE CARLO METHOD
NONLINEAR PROBLEMS
Physical properties
STAR EVOLUTION
Star formation
STARS
Strategy
TIME DEPENDENCE
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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.
AbstractList 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 Viti, Serena
Makrymallis, Antonios
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SubjectTerms ALGORITHMS
ASTROPHYSICS, COSMOLOGY AND ASTRONOMY
Bayesian analysis
DATA ANALYSIS
Evolution
ICE
Inverse problems
MARKOV PROCESS
Mathematical models
MATHEMATICAL SOLUTIONS
Molecular clouds
MONTE CARLO METHOD
NONLINEAR PROBLEMS
Physical properties
STAR EVOLUTION
Star formation
STARS
Strategy
TIME DEPENDENCE
Title UNDERSTANDING THE FORMATION AND EVOLUTION OF INTERSTELLAR ICES: A BAYESIAN APPROACH
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