The impact of void-finding algorithms on galaxy classification (2403.20008v1)

Abstract: We explore how the definition of a void influences the conclusions drawn about the impact of the void environment on galactic properties using two void-finding algorithms in the Void Analysis Software Toolkit: V2, a Python implementation of ZOBOV, and VoidFinder, an algorithm which grows and merges spherical void regions. Using the Sloan Digital Sky Survey Data Release 7, we find that galaxies found in VoidFinder voids tend to be bluer, fainter, and have higher (specific) star formation rates than galaxies in denser regions. Conversely, galaxies found in V2 voids show no significant differences when compared to galaxies in denser regions, inconsistent with the large-scale environmental effects on galaxy properties expected from both simulations and previous observations. These results align with previous simulation results that show V2-identified voids "leaking" into the dense walls between voids because their boundaries extend up to the density maxima in the walls. As a result, when using ZOBOV-based void finders, galaxies likely to be part of wall regions are instead classified as void galaxies, a misclassification that can be critical to our understanding of galaxy evolution.