Constraining DSFG Multiplicity and Clustering with Comparisons of Empirical Submillimeter Map Models

DSFG multiplicity, the spatial blending of sources' flux densities due to the low spatial resolution of FIR/submm maps, can be attributed to chance projections of sources, physically associated sources, or a combination of both. If DSFG multiplicity is predominantly due to chance projections of sources along the line of sight as opposed to physically associated sources, this effect would impact our interpretation of DSFG evolution. In particular, cosmological simulations struggle to reproduce DSFGs as single bright sources; a significant population of DSFG multiples would ease tension between cosmological predictions and DSFG observations, as each DSFG would have an effectively lower SFR (e.g. Hayward et al. 2013). The goal of this project is to compare results of predictive empirical models of (sub)millimeter emission across the literature to constrain the role of chance projections of physically unassociated sources with simulated data. Specifically, we compare mock output of the models of Casey et al. 2018a,b -- which is an empirical model of the (sub)mm sky which does not account for cosmological clustering but allows for variable histories of obscured cosmic star formation -- with the SIDES model described by Bethermin et al. 2017 who account for cosmological clustering yet only one possible history of obscured cosmic star-formation at early times. Our results indicate that the high single-dish flux density sources across all submm/mm wavebands can be mostly (>70%) attributed to a single bright source, whereas lower flux density sources are statistically more likely to be a compilation of several lower flux density sources. We recover the same multiplicity fraction in both the Casey et al. and Bethermin et al. models, despite the difference in clustering prescriptions between them; thus we conclude that the cause of DSFG multiplicity (line-of-sight projection or physical associated pairs) is not directly constrainable through empirical models alone.