Pith. sign in

REVIEW 1 cited by

The Three Hundred Project: Backsplash galaxies in simulations of clusters

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2001.11518 v1 pith:IHQNZBHM submitted 2020-01-30 astro-ph.GA

The Three Hundred Project: Backsplash galaxies in simulations of clusters

classification astro-ph.GA
keywords clustergalaxiesbacksplashclustersfractioncentredependentdynamical
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

In the outer regions of a galaxy cluster, galaxies may be either falling into the cluster for the first time, or have already passed through the cluster centre at some point in their past. To investigate these two distinct populations, we utilise TheThreeHundred project, a suite of 324 hydrodynamical resimulations of galaxy clusters. In particular, we study the 'backsplash population' of galaxies; those that have passed within $R_{200}$ of the cluster centre at some time in their history, but are now outside of this radius. We find that, on average, over half of all galaxies between $R_{200}$ and $2R_{200}$ from their host at $z=0$ are backsplash galaxies, but that this fraction is dependent on the dynamical state of a cluster, as dynamically relaxed clusters have a greater backsplash fraction. We also find that this population is mostly developed at recent times ($z\leq0.4$), and is dependent on the recent history of a cluster. Finally, we show that the dynamical state of a given cluster, and thus the fraction of backsplash galaxies in its outskirts, can be predicted based on observational properties of the cluster.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Identifying backsplash galaxies using machine learning

    astro-ph.GA 2026-07 conditional novelty 6.0

    Machine learning trained on The Three Hundred simulations identifies backsplash galaxies in cluster outskirts with ~75% purity/completeness, and has been applied to HI-tail galaxies in Virgo.