TL;DR
This paper develops a Bayesian framework to constrain chemical and dust evolution parameters in nearby galaxies, revealing degeneracies and the dominant dust production mechanisms across metallicities.
Contribution
It introduces a comprehensive Bayesian Monte Carlo Markov Chain approach to analyze a large set of galaxy models against observational data, improving parameter constraints.
Findings
Dust production shifts from supernovae to grain growth at higher metallicities.
The transition metallicity is around 12+log(O/H)=7.75, lower than previous estimates.
The fraction of metals available for grain growth is well constrained.
Abstract
We build a rigorous statistical framework to provide constraints on the chemical and dust evolution parameters for nearby late-type galaxies with a wide range of gas fractions (). A Bayesian Monte Carlo Markov Chain framework provides statistical constraints on the parameters used in chemical evolution models. Nearly a million one-zone chemical and dust evolution models were compared to 340 galaxies. Relative probabilities were calculated from the between data and models, marginalised over the different time steps, galaxy masses and star formation histories. We applied this method to find `best fitting' model parameters related to metallicity, and subsequently fix these metal parameters to study the dust parameters. For the metal parameters, a degeneracy was found between the choice of initial mass function, supernova metal yield tables and outflow prescription.…
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