The galaxy spectral energy distributions (SEDs) from far-UV to far-IR are very important source of information about the properties of its stellar population, interstellar gas and dust, and AGN. To better understand the complex interplay among the three important physical components during the formation and evolution of galaxies, we need a reliable and efficient method and tool to extract useful information about them from the huge amount of data sets stemming from both ground- and space-based missions. To this end, with the combination of machine learning techniques and Bayesian inference, we have built the BayeSED code. In this talk, I will introduce the next generation of our BayeSED code which is capable of efficiently modeling and interpreting the full far-UV to far-IR SEDs of galaxies.

BayeSED code, including the machine learning module, is publicly available at https://bitbucket.org/hanyk/bayesed/. See also the documents at http://bayesed.readthedocs.io/.