Bayesian MCMC galaxy SED modeling -cont.

It is interesting to see that very recently the following three papers are posted on astro-ph. They are about SED fitting using Markov chain Monte Carlo.  

1.  2.  3.

They’re sharing the same motivation: the correct characterization of galaxy SED model parameter space including the degeneracy and parameter covariance which has been intrinsically difficult to reveal by conventional “chi^2” minimization-based best fit parameter approach. Although they are using different population synthesis models, all of them demonstrated the existence of the severe degeneracy between some model parameters.

These results again motivates the full Bayesian approach with advanced MCMC sampler, which can potentially achieve the following improvements.

1. Parameter constraint power can be improved by informative prior. For example, the mm/sub-mm SED modeling has a degeneracy between redshift and dust temperature, which might be broken by constraining the redshift using other part of SED (i.e. optical or emission lines). The Bayesian approach allows us to model a subset of data, then later to add more data to improve the inference. So one can first model the optical SED and use the posterior as a new prior when modeling the other part of SED (e.g. mm/sub-mm). This approach helps to constraint the model parameters with strong degeneracy.

2. The different hypothesis can be tested using the Bayes factor. For example, one can test whether the two stellar population is better description than the single stellar population or not. In addition, the different forms of IMF can be tested against the data over different redshift or environment. 

Although I expect that SED modeling is highly non-linear compare to the GALPHAT galaxy morphology modeling, the full Bayesian approach using the BIE will certainly improve our inference which is still poor in some cases, using even MCMC approach recently developed by these several groups.


~ by ilsangyoon on April 3, 2011.

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