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The forthcoming CSST wide-field multiband imaging survey will produceseven-band photometric spectral energy distributions (SEDs) for billions ofgalaxies. The effective extraction of astronomical information from thesemassive datasets of SEDs relies on the techniques of both SED synthesis (ormodeling) and analysis (or fitting). We evaluate the performance of the latestversion of BayeSED code combined with SED models with increasing complexity forsimultaneously determining the photometric redshifts and stellar populationparameters of galaxies in this survey. By using an empirical statistics-basedmock galaxy sample without SED modeling errors, we show finding that the randomobservational errors in photometries are more important sources of errors thanthe parameter degeneracies and Bayesian analysis method and tool. By using aHorizon-AGN hydrodynamical simulation-based mock galaxy sample with SEDmodeling errors about the star formation histories (SFHs) and dust attenuationlaws (DALs), the simple typical assumptions lead to significantly worseparameter estimation with CSST photometries only. The SED models with moreflexible (or complicated) forms of SFH/DAL do not necessarily lead to betterestimation of redshift and stellar population parameters. We discuss theselection of the best SED model by means of Bayesian model comparison indifferent surveys. Our results reveal that the Bayesian model comparison withBayesian evidence may favor SED models with different complexities when usingphotometries from different surveys. Meanwhile, the SED model with the largestBayesian evidence tends to give the best performance of parameter estimation,which is more clear for photometries with larger discriminative power.

BayeSED-GALAXIES I. Performance test for simultaneous photometric  redshift and stellar population parameter estimation of galaxies in the CSST  wide-field multiband imaging survey