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The Three Hundred Project: The gas disruption of infalling objects in cluster environments

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arxiv 2101.01734 v1 pith:AZLOACWJ submitted 2021-01-05 astro-ph.GA astro-ph.CO

The Three Hundred Project: The gas disruption of infalling objects in cluster environments

classification astro-ph.GA astro-ph.CO
keywords haloesinfallingclusterdisruptionhostinfalltrendsenvironments
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We analyse the gas content evolution of infalling haloes in cluster environments from THE THREE HUNDRED project, a collection of 324 numerically modelled galaxy clusters. The haloes in our sample were selected within $5R_{200}$ of the main cluster halo at $z=0$ and have total halo mass $M_{200}\geq10^{11} h^{-1} M_{\odot}$. We track their main progenitors and study their gas evolution since their crossing into the infall region, which we define as $1-4R_{200}$. Studying the radial trends of our populations using both the full phase space information and a line-of-sight projection, we confirm the Arthur et al. (2019) result and identify a characteristic radius around $1.7R_{200}$ in 3D and at $R_{200}$ in projection at which infalling haloes lose nearly all of the gas prior their infall. Splitting the trends by subhalo status we show that subhaloes residing in group-mass and low-mass host haloes in the infall region follow similar radial gas-loss trends as their hosts, whereas subhaloes of cluster-mass host haloes are stripped of their gas much further out. Our results show that infalling objects suffer significant gaseous disruption that correlates with time-since-infall, cluster-centric distance and host mass, and that the gaseous disruption they experience is a combination of subhalo pre-processing and object gas depletion at a radius which behaves like an accretion shock.

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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.