Using large galaxy surveys to distinguish z~0.5 quiescent galaxy models
arXiv:1311.0850 · doi:10.1093/mnras/stu367
Abstract
One of the most striking properties of galaxies is the bimodality in their star-formation rates. A major puzzle is why any given galaxy is star-forming or quiescent, and a wide range of physical mechanisms have been proposed as solutions. We consider how observations, such as might be available in upcoming large galaxy surveys, might distinguish different galaxy quenching scenarios. To do this, we combine an N-body simulation and multiple prescriptions from the literature to create several quiescent galaxy mock catalogues. Each prescription uses a different set of galaxy properties (such as history, environment, centrality) to assign individual simulation galaxies as quiescent. We find how and how much the resulting quiescent galaxy distributions differ from each other, both intrinsically and observationally. In addition to tracing observational consequences of different quenching mechanisms, our results indicate which sorts of quenching models might be most readily disentangled by upcoming observations and which combinations of observational quantities might provide the most discriminatory power. Our observational measures are auto, cross, and marked correlation functions, pro- jected density distributions, and group multiplicity functions, which rely upon galaxy positions, stellar masses and of course quiescence. Although degeneracies between models are present for individual observations, using multiple observations in concert allows us to distinguish between all ten models we consider. In addition to identifying intrinsic and observational consequences of quiescence prescriptions and testing these quiescence models against each other and observations, these methods can also be used to validate colors (or other history and environment dependent properties) in simulated mock catalogues.
20 pages, 14 figures. Version to appear in MNRAS, incorporating helpful suggestions from referee and others
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