Superclusters from velocity divergence fields
  Superclusters are a convenient way to partition and characterize the large
scale structure of the Universe. In this Letter we explore the advantages of
defining superclusters as watershed basins in the divergence velocity field. We
apply this definition on diverse datasets generated from linear theory and
N-body simulations, with different grid sizes, smoothing scales and types of
tracers. From this framework emerges a linear scaling relation between the
average supercluster size and the autocorrelation length in the divergence
field, a result that holds for one order of magnitude from 10 Mpc/h up to 100
Mpc/h. These results suggest that the divergence-based definition provides a
robust context to quantitatively compare results across different observational
or computational frameworks. Through its connection with linear theory, it can
also facilitate the exploration of how supercluster properties depend on
cosmological parameters, paving the way to use superclusters as cosmological
probes.
