The impact of galaxy selection on the splashback boundaries of galaxy clusters

Authors: Stephanie O'Neil (MIT), Josh Borrow (MIT), Mark Vogelsberger (MIT), Benedikt Diemer (UMD)

arXiv: 2202.05277v2 - DOI (astro-ph.GA)
18 pages, 18 figures, submitted to MNRAS

Abstract: We explore how the splashback radius ($R_{\rm sp}$) of galaxy clusters, measured using the number density of the subhalo population, changes based on various selection criteria using the IllustrisTNG cosmological galaxy formation simulation. We identify $R_{\rm sp}$ by extracting the steepest radial gradient in a stacked set of clusters in 0.5 dex wide mass bins, with our clusters having halo masses $10^{13} \leq M_{\rm 200, mean} / {\rm M}_\odot \leq 10^{15}$. We apply cuts in subhalo mass, galaxy stellar mass, $i$-band absolute magnitude and specific star formation rate. We find that, generally, galaxies of increasing mass and luminosity trace smaller measured splashback radii relative to the intrinsic dark matter radius. We also show that quenched galaxies may be used to reliably reconstruct the dark matter splashback radius. This trend is likely due to changes in the galaxy population. Additionally, we are able to reconcile different observational predictions that $R_{\rm sp}$ based upon galaxy number counts and dark matter may either align or show significant offset (e.g. those using optically- or SZ-selected clusters) through the selection functions that these studies employ. Finally, we demonstrate that changes in $R_{\rm sp}$ measured through number counts are not due to a simple change in galaxy abundance inside and outside of the cluster.

Submitted to arXiv on 10 Feb. 2022

Explore the paper tree

Click on the tree nodes to be redirected to a given paper and access their summaries and virtual assistant

Also access our AI generated Summaries, or ask questions about this paper to our AI assistant.

Look for similar papers (in beta version)

By clicking on the button above, our algorithm will scan all papers in our database to find the closest based on the contents of the full papers and not just on metadata. Please note that it only works for papers that we have generated summaries for and you can rerun it from time to time to get a more accurate result while our database grows.