Detecting episodes of star formation using Bayesian model selection.


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Bayesian model comparison is a data-driven method to establish model complexity. In this dissertation we investigate its use in detecting multiple episodes of star formation from the analysis of the Spectral Energy Distribution (SED) of galaxies. This method is validated by simulating galaxy catalogs modeled after 3D-HST galaxies at redshift z ∼ 1. The SED of galaxies are derived using multivariate kernel density estimates of the input parameter distributions before fitting results and Bayes factors for multiple scenarios of nested models. In addition, we investigate the role that prior specification has in the derivation of physical parameters. These results are then compared to Bayes factors calculated using the Savage-Dickey Density Ratio (SDDR). The results of this investigation indicate that the use of Bayes factors are a promising tool when the model has a high level of complexity. We also demonstrate that the choice of priors plays an important role in the accuracy of results and that the SDDR is a good proxy for the Bayes factor. This last finding has significant computational advantages when compared with the computationally intensive nested sampling algorithms.



Spectral energy distribution. Bayes factor. Savage-Dickey density ratio. Star formation.