GalMC: A Markov Chain Monte Carlo algorithm for SED fitting

Thanks for your interest in GalMC!

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Note: This material is based upon work supported by the National Science Foundation under Grant No. 0807570. Any opinions, findings and conclusions o recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF).

**GalMC has now been retired**because I can't maintain it any more.These are some of the many great alternatives: see also here.

**Why:**The purpose of this code is to obtain reliable estimates of the values of parameters used in Spectral Energy Distribution fitting and their uncertainties, in a Bayesian framework. Starting from photometric multi-wavelength observations of a galaxy, GalMC compares the data to stellar population synthesis templates, exploring the parameter space in a way which is biased toward high-probability regions. The final collection of samples in parameter space is drawn from the multi-dimensional probability distribution function of the parameters. As a result, marginalization over parameters, usually a lengthy integral in many dimensions, becomes an easy sum and produces the 1-D (or 2-D) posterior probability functions, like the one that you see in the above cartoon. This process is more CPU efficient with respect to grid-based methods, produces unbiased results, and is effective in revealing degeneracies between parameters.**What:**At this time (v 1.2) GalMC can fit for six SED parameters: age, stellar mass, metallicity, redshift, dust content parametrized by the excess color E(B-V), and the e-folding time characteristic of exponential star formation histories. You can pick the SFH to be an instantaneous starburst, constant, or exponentially declining/increasing. GalMC uses stellar population synthesis templates from Charlot and Bruzual 2010 or Bruzual and Charlot 2003. More details are provided in the documentation files.Note: This material is based upon work supported by the National Science Foundation under Grant No. 0807570. Any opinions, findings and conclusions o recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF).