Constructing a Galaxy Cluster Catalog in IllustrisTNG300 Using the Mulguisin Algorithm

We present a new simulated galaxy cluster catalog based on the IllustrisTNG simulation. We use the Mulguisin (MGS) algorithm to identify galaxy overdensities. Our cluster identification differs from the previous friends-of-friends (FoF) cluster identification in two aspects: (1) we identify cluster...

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Veröffentlicht in:The Astrophysical journal 2024-10, Vol.974 (2), p.252
Hauptverfasser: Shin, Lael, Sohn, Jubee, Ju, Young, Park, Inkyu, Sabiu, Cristiano G.
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Sprache:eng
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Zusammenfassung:We present a new simulated galaxy cluster catalog based on the IllustrisTNG simulation. We use the Mulguisin (MGS) algorithm to identify galaxy overdensities. Our cluster identification differs from the previous friends-of-friends (FoF) cluster identification in two aspects: (1) we identify cluster halos based on the galaxy subhalos instead of unobservable dark matter particles, and (2) we use the MGS algorithm, which separates galaxy overdensities hosted by massive galaxies. Our approach provides a cluster catalog constructed in a way similar to the construction of observed cluster catalogs using spectroscopic surveys. The MGS cluster catalog lists 303 halos with M 200 > 10 14 M ⊙ , including ∼10% more than the FoF catalog. The MGS catalog includes more systems because we separate some independent massive MGS cluster halos that are bundled into a single FoF halo. These independent MGS halos are apparently distinguishable in the galaxy spatial distribution and the phase-space diagram. Because we construct a refined cluster catalog that identifies local galaxy overdensities, we evaluate the effect of MGS clusters on the evolution of galaxies better than when using the FoF cluster catalog. The MGS halo identification also enables effective identifications of merging clusters by selecting systems with neighboring galaxy overdensities. We thus highlight the fact that the MGS cluster catalog is a useful tool for studying clusters in cosmological simulations and for comparing with observed cluster samples.
ISSN:0004-637X
1538-4357
DOI:10.3847/1538-4357/ad72e8