Superclusters from velocity divergence fields

TL;DR

This paper proposes defining superclusters as watershed basins in the divergence velocity field, revealing a linear relation between supercluster size and autocorrelation length, and demonstrating its robustness across datasets.

Contribution

It introduces a divergence-based watershed method for defining superclusters, enabling consistent comparison and potential cosmological applications.

Findings

Linear scaling relation between supercluster size and divergence field autocorrelation length.

Robustness of the divergence-based supercluster definition across datasets.

Potential for using superclusters as cosmological probes.

Abstract

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.