The Impact of Void-Finding Algorithms on Galaxy Classification

We explore how the definition of a void influences the conclusions drawnabout the impact of the void environment on galactic properties using twovoid-finding algorithms in the Void Analysis Software Toolkit: V2, a Pythonimplementation of ZOBOV, and VoidFinder, an algorithm which grows and mergesspherical void regions. Using the Sloan Digital Sky Survey Data Release 7, wefind that galaxies found in VoidFinder voids tend to be bluer, fainter, andhave higher (specific) star formation rates than galaxies in denser regions.Conversely, galaxies found in V2 voids show no significant differences whencompared to galaxies in denser regions, inconsistent with the large-scaleenvironmental effects on galaxy properties expected from both simulations andprevious observations. These results align with previous simulation resultsthat show V2-identified voids "leaking" into the dense walls between voidsbecause their boundaries extend up to the density maxima in the walls. As aresult, when using ZOBOV-based void finders, galaxies likely to be part of wallregions are instead classified as void galaxies, a misclassification that canbe critical to our understanding of galaxy evolution.