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The estimated stellar masses of galaxies are widely used to characterize howthe galaxy population evolves over cosmic time. If stellar masses can beestimated in a robust manner, free from any bias, global diagnostics such asthe stellar mass function can be used to constrain the physics of galaxyformation. We explore how galaxy stellar masses, estimated by fittingbroad-band spectral energy distributions (SEDs) with stellar population models,can be biased as a result of commonly adopted assumptions for thestar-formation and chemical enrichment histories, recycled fractions and dustattenuation curves of galaxies. We apply the observational technique ofbroad-band SED fitting to model galaxy SEDs calculated by the theoreticalgalaxy formation model GALFORM, isolating the effect of each of theseassumptions. We find that, averaged over the entire galaxy population, thecommon assumption of exponentially declining star-formation histories does notadversely affect stellar mass estimation. We show that fixing the metallicityin SED fitting or using sparsely sampled metallicity grids can introduce massdependent systematics into stellar mass estimates. We find that the commonassumption of a star-dust geometry corresponding to a uniform foreground dustscreen can cause the stellar masses of dusty model galaxies to be significantlyunderestimated. Finally, we show that stellar mass functions recovered byapplying SED fitting to model galaxies at high redshift can differsignificantly in both shape and normalization from the intrinsic mass functionspredicted by a given model. Given these differences, our methodology of usingstellar masses estimated from model galaxy SEDs offers a new, self-consistentway to compare model predictions with observations.

How well can we really estimate the stellar masses of galaxies from broadband photometry?