We present BayeSED, a general purpose tool for doing Bayesian analysis of SEDs by using whatever pre-existing model SED libraries or their linear combinations. The artificial neural networks (ANNs), principal component analysis (PCA) and multimodal nested sampling (MultiNest) techniques are employed to allow a highly efficient sampling of posterior distribution and the calculation of Bayesian evidence. As a demonstration, we apply this tool to a sample of hyperluminous infrared galaxies (HLIRGs). The Bayesian evidences obtained for a pure Starburst, a pure AGN, and a linear combination of Starburst+AGN models show that the Starburst+AGN model have the highest evidence for all galaxies in this sample. The Bayesian evidences for the three models and the estimated contributions of starburst and AGN to infrared luminosity show that HLIRGs can be classified into two groups: one dominated by starburst and the other dominated by AGN. Other parameters and corresponding uncertainties about starburst and AGN are also estimated by using the model with the highest Bayesian evidence. We found that the starburst region of the HLIRGs dominated by starburst tends to be more compact and has a higher fraction of OB star than that of HLIRGs dominated by AGN. Meanwhile, the AGN torus of the HLIRGs dominated by AGN tend to be more dusty than that of HLIRGs dominated by starburst. These results are consistent with previous researches, but need to be tested further with larger samples. Overall, we believe that BayeSED could be a reliable and efficient tool for exploring the nature of complex systems such as dust-obscured starburst-AGN composite systems from decoding their SEDs.

We present a newly developed version of BayeSED, a general Bayesian approach to the spectral energy distribution (SED) fitting of galaxies. The new BayeSED code has been systematically tested on a mock sample of galaxies. The comparison between estimated and inputted value of the parameters show that BayeSED can recover the physical parameters of galaxies reasonably well. We then applied BayeSED to interpret the SEDs of a large Ks-selected sample of galaxies in the COSMOS/UltraVISTA field with stellar population synthesis models. With the new BayeSED code, a Bayesian model comparison of stellar population synthesis models has been done for the first time. We found that the model by Bruzual & Charlot (2003), statistically speaking, has larger Bayesian evidence than the model by Maraston (2005) for the Ks-selected sample. Besides, while setting the stellar metallicity as a free parameter obviously increases the Bayesian evidence of both models, varying the IMF has a notable effect only on the Maraston (2005) model. Meanwhile, the physical parameters estimated with BayeSED are found to be generally consistent with those obtained with the popular grid-based FAST code, while the former exhibits more natural distributions. Based on the estimated physical parameters of galaxies in the sample, we qualitatively classified the galaxies in the sample into five populations that may represent galaxies at different evolution stages or in different environments. We conclude that BayeSED could be a reliable and powerful tool for investigating the formation and evolution of galaxies from the rich multi-wavelength observations currently available. A binary version of MPI parallelized BayeSED code is publicly available at https://bitbucket.org/hanyk/bayesed.