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Characterising the intra-cluster light in The Three Hundred simulations

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arxiv 2401.08283 v1 pith:OGT2YE3Q submitted 2024-01-16 astro-ph.CO astro-ph.GA

classification astro-ph.COastro-ph.GA
keywords clusterclustersmassfractiongalaxyfindhundredintra-cluster
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abstract

We characterise the intra-cluster light (ICL) in ensembles of full-physics cluster simulations from The Three Hundred project, a suite of 324 hydrodynamical resimulations of cluster-sized halos. We identify the ICL as those stellar particles bound to the potential of the cluster itself, but not to any of its substructures, and separate the brightest cluster galaxy (BCG) by means of a fixed 50 kpc aperture. We find the total BCG+ICL mass to be in agreement with state-of-the-art observations of galaxy clusters. The ICL mass fraction of our clusters is between 30 and 50 per cent of the total stellar mass within $R_{500}$, while the BCG represents around 10 percent. We further find no trend of the ICL fraction with cluster halo mass, at least not in the range $[0.2,3]\cdot10^{15}h^{-1}M_\odot$ considered here. For the dynamical state, characterised both by theoretical estimators and by the recent merging history of the cluster, there is a clear correlation, such that more relaxed clusters and those that have undergone fewer recent mergers have a higher ICL fraction. Finally, we investigate the possibility of using the ICL to explore the dark matter (DM) component of galaxy clusters. We compute the volumetric density profile for the DM and ICL components and show that, up to $R_{500}$, the ratio between the two can be described by a power law. Working with the velocity dispersion profiles instead, we show that the ratio can be fit by a straight line. Providing the parameters of these fits, we show how the ICL can be used to infer DM properties.

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  1. Predicting Halo Formation Time Using Machine Learning

    astro-ph.CO 2025-04 conditional novelty 6.0 of 10

    Halo formation times can be predicted from observable halo and galaxy properties with roughly 5 percent bias and 20 percent scatter using machine learning models trained on cosmological simulations.

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