Bayesian MCMC galaxy SED modeling
In these days, galaxy SED modeling becomes popular since the current and forthcoming large scale surveys will produce multi-bands photometric data points sufficient to sample the galaxy integrated spectral energy distribution, which allows us to infer many interesting aspects of galaxy properties including stellar mass, star formation history, dust contents etc.
Currently there are several versions of widely used software package to model the galaxy spectra (i.e. stellar population thesis code). However the model parameter space is large and not well constrained, and thus in most cases, people use the population synthesis code with very strong assumptions about IMF and star formation history, which significantly affect our inference of galaxy stellar mass.
To improve this situation, Bayesian MCMC approach to model the galaxy integrated SED is very powerful solution not only for estimating the parameter and its uncertainty but also for assessing the statistical power of different models based on different assumptions made by user (different IMF, star formation history and multiple population etc.), using the Bayes factor model selection.
The successful demonstration of this approach to the galaxy morphology analysis (GALPHAT) convinces me that it will be straight forward to apply BIE to galaxy SED modeling. This is certainly my next project in the near future.
Link to the publically avaliable stellar population synthesis codes
CIGALE : SED fitting code
www.sedfitting.org : useful site