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Reconstructing Orbits of Galaxies in Extreme Regions (ROGER) III: galaxy evolution patterns in projected phase space around massive X-ray clusters

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arxiv 2212.09780 v1 pith:SMNQZAVO submitted 2022-12-19 astro-ph.GA

Reconstructing Orbits of Galaxies in Extreme Regions (ROGER) III: galaxy evolution patterns in projected phase space around massive X-ray clusters

classification astro-ph.GA
keywords galaxiesclassesclusterformationstarblueclustersevidence
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We use the ROGER code by de los Rios et al. to classify galaxies around a sample of X-ray clusters into five classes according to their positions in the projected phase space diagram: cluster galaxies, backsplash galaxies, recent infallers, infalling galaxies, and interlopers. To understand the effects of the cluster environment to the evolution of galaxies, we compare across the five classes: stellar mass, specific star formation rate, size, and morphology. Following the guidelines of Coenda et al., a separate analysis is carried out for red and blue galaxies. For red galaxies, cluster galaxies differ from the other classes, having a suppressed specific star formation rate, smaller sizes, and are more likely to be classified as ellipticals. Differences are smaller between the other classes, however backsplash galaxies have significantly lower specific star formation rates than early or recent infalling galaxies. For blue galaxies, we find evidence that recent infallers are smaller than infalling galaxies and interlopers, while the latter two are comparable in size. Our results provide evidence that, after a single passage, the cluster environment can diminish a galaxy's star formation, modify its morphology, and can also reduce in size blue galaxies. We find evidence that quenching occurs faster than morphological transformation from spirals to ellipticals for all classes. While quenching is evidently enhanced as soon as galaxies get into clusters, significant morphological transformations require galaxies to experience the action of the physical mechanisms of the cluster for longer timescales.

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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.