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When modeling and interpreting the spectral energy distributions (SEDs) ofgalaxies, the simple stellar population (SSP) model, star formation history(SFH) and dust attenuation law (DAL) are three of the most importantcomponents. However, each of them carries significant uncertainties which haveseriously limited our ability to reliably recover the physical properties ofgalaxies from the analysis of their SEDs. In this paper, we present a Bayesianframework to deal with these uncertain components simultaneously. Based on theBayesian evidence, a quantitative implement of the principle of Occam's razor,the method allows a more objective and quantitative discrimination among thedifferent assumptions about these uncertain components. With a Ks-selectedsample of 5467 low-redshift (mostly with $z\lesssim 1$) galaxies in theCOSMOS/UltraVISTA field and classified into passively evolving galaxies (PEGs)and star-forming galaxies (SFGs) with UVJ diagram, we present a Bayesiandiscrimination of a set of 16 SSP models from five research groups (BC03 andCB07, M05, GALEV, Yunnan-II, BPASS V2.0), five forms of SFH (Burst, Constant,Exp-dec, Exp-inc, Delayed-$\tau$), and four kinds of DAL (Calzetti law, MW,LMC, SMC). We show that the results obtained with the method are either obviousor understandable in the context of galaxy physics. We conclude that theBayesian model comparison method, especially that for a sample of galaxies, isvery useful for discriminating the different assumptions in the SED modeling ofgalaxies. The new version of the BayeSED code, which is used in this work, ispublicly available at https://bitbucket.org/hanyk/bayesed/.

the astrophysical journal. supplement series/astrophysical journal. supplement series

A Comprehensive Bayesian Discrimination of the Simple Stellar Population  Model, Star Formation History and Dust Attenuation Law in the Spectral Energy  Distribution Modeling of Galaxies