We present BayeSED, a general purpose tool for doing Bayesian analysis ofSEDs by using whatever pre-existing model SED libraries or their linearcombinations. The artificial neural networks (ANNs), principal componentanalysis (PCA) and multimodal nested sampling (MultiNest) techniques areemployed to allow a highly efficient sampling of posterior distribution and thecalculation of Bayesian evidence. As a demonstration, we apply this tool to asample of hyperluminous infrared galaxies (HLIRGs). The Bayesian evidencesobtained for a pure Starburst, a pure AGN, and a linear combination ofStarburst+AGN models show that the Starburst+AGN model have the highestevidence for all galaxies in this sample. The Bayesian evidences for the threemodels and the estimated contributions of starburst and AGN to infraredluminosity show that HLIRGs can be classified into two groups: one dominated bystarburst and the other dominated by AGN. Other parameters and correspondinguncertainties about starburst and AGN are also estimated by using the modelwith the highest Bayesian evidence. We found that the starburst region of theHLIRGs dominated by starburst tends to be more compact and has a higherfraction of OB star than that of HLIRGs dominated by AGN. Meanwhile, the AGNtorus of the HLIRGs dominated by AGN tend to be more dusty than that of HLIRGsdominated by starburst. These results are consistent with previous researches,but need to be tested further with larger samples. Overall, we believe thatBayeSED could be a reliable and efficient tool for exploring the nature ofcomplex systems such as dust-obscured starburst-AGN composite systems fromdecoding their SEDs.
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