Pith. sign in

REVIEW 1 cited by

The Preferential Tidal Stripping of Dark Matter versus Stars in Galaxies

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 1610.04264 v1 pith:L2F4YEVS submitted 2016-10-13 astro-ph.GA

classification astro-ph.GA
keywords darkmatterstarsstrippinggalaxiesmasstidalbound
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
abstract

Using high resolution hydrodynamical cosmological simulations, we conduct a comprehensive study of how tidal stripping removes dark matter and stars from galaxies. We find that dark matter is always stripped far more significantly than the stars -- galaxies that lose $\sim$80$\%$ of their dark matter, typically lose only 10$\%$ of their stars. This is because the dark matter halo is initially much more extended than the stars. As such, we find the stellar-to-halo size-ratio (measured using r$_{\rm{eff}}$/r$_{\rm{vir}}$) is a key parameter controlling the relative amounts of dark matter and stellar stripping. We use simple fitting formulae to measure the relation between the fraction of bound dark matter and fraction of bound stars. We measure a negligible dependence on cluster mass or galaxy mass. Therefore these formulae have general applicability in cosmological simulations, and are ideal to improve stellar stripping recipes in semi-analytical models, and/or to estimate the impact that tidal stripping would have on galaxies when only their halo mass evolution is known.

Discussion (0). Sign in to comment.

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 of 10

    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.

Pith tools