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Register a new userA deep learning view of the census of galaxy clusters in IllustrisTNG
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The origin of the diverse population of galaxy clusters remains an unexplained aspect of large-scale structure formation and cluster evolution. We present a novel method of using X-ray images to identify cool core (CC), weak cool core (WCC), and non cool core (NCC) clusters of galaxies, that are defined by their central cooling times. We employ a convolutional neural network, ResNet-18, which is commonly used for image analysis, to classify clusters. We produce mock Chandra X-ray observations for a sample of 318 massive clusters drawn from the IllustrisTNG simulations. The network is trained and tested with low resolution mock Chandra images covering a central 1 Mpc square for the clusters in our sample. Without any spectral information, the deep learning algorithm is able to identify CC, WCC, and NCC clusters, achieving balanced accuracies (BAcc) of 92%, 81%, and 83%, respectively. The performance is superior to classification by conventional methods using central gas densities, with an average BAcc = 81%, or surface brightness concentrations, giving BAcc = 73%. We use Class Activation Mapping to localize discriminative regions for the classification decision. From this analysis, we observe that the network has utilized regions from cluster centers out to r~300 kpc and r~500 kpc to identify CC and NCC clusters, respectively. It may have recognized features in the intracluster medium that are associated with AGN feedback and disruptive major mergers.
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The thermodynamic structure of hot gas in galaxy clusters is sensitive to astrophysical processes and typically difficult to model with galaxy formation simulations. We explore the fraction of cool-core (CC) clusters in a large sample of $370$ clusters from IllustrisTNG, examining six common CC definitions. IllustrisTNG produces continuous CC criteria distributions, the extremes of which are classified as CC and non-cool-core (NCC), and the criteria are increasingly correlated for more massive clusters. At $z=0$, the CC fractions for $2$ criteria are in reasonable agreement with the observed fractions but the other $4$ CC fractions are lower than observed. This result is partly driven by systematic differences between the simulated and observed gas fraction profiles. The simulated CC fractions with redshift show tentative agreement with the observed fractions, but linear fits demonstrate that the simulated evolution is steeper than observed. The conversion of CCs to NCCs appears to begin later and act more rapidly in the simulations. Examining the fraction of CCs and NCCs defined as relaxed we find no evidence that CCs are more relaxed, suggesting that mergers are not solely responsible for disrupting CCs. A comparison of the median thermodynamic profiles defined by different CC criteria shows that the extent to which they evolve in the cluster core is dependent on the CC criteria. We conclude that the thermodynamic structure of galaxy clusters in IllustrisTNG shares many similarities with observations, but achieving better agreement most likely requires modifications of the underlying galaxy formation model.
Ultra-compact dwarf galaxies (UCDs) are predominatly found in the cores of nearby galaxy clusters. Besides the Fornax and Virgo cluster, UCDs have also been confirmed in the twice as distant Hydra I and Centaurus clusters. Having (nearly) complete samples of UCDs in some of these clusters allows the study of the bulk properties with respect to the environment they are living in. Moreover, the relation of UCDs to other stellar systems in galaxy clusters, like globular clusters and dwarf ellipticals, can be investigated in detail with the present data sets. The general finding is that UCDs seem to be a heterogenous class of objects. Their spatial distribution within the clusters is in between those of globular clusters and dwarf ellipticals. In the colour-magnitude diagram, blue/metal-poor UCDs coincide with the sequence of nuclear star clusters, whereas red/metal-rich UCDs reach to higher masses and might have originated from the amalgamation of massive star cluster complexes in merger or starburst galaxies.
Galaxy clusters identified from the Sunyaev Zeldovich (SZ) effect are a key ingredient in multi-wavelength cluster-based cosmology. We present a comparison between two methods of cluster identification: the standard Matched Filter (MF) method in SZ cluster finding and a method using Convolutional Neural Networks (CNN). We further implement and show results for a `combined identifier. We apply the methods to simulated millimeter maps for several observing frequencies for an SPT-3G-like survey. There are some key differences between the methods. The MF method requires image pre-processing to remove point sources and a model for the noise, while the CNN method requires very little pre-processing of images. Additionally, the CNN requires tuning of hyperparameters in the model and takes as input, cutout images of the sky. Specifically, we use the CNN to classify whether or not an 8 arcmin $times$ 8 arcmin cutout of the sky contains a cluster. We compare differences in purity and completeness. The MF signal-to-noise ratio depends on both mass and redshift. Our CNN, trained for a given mass threshold, captures a different set of clusters than the MF, some of which have SNR below the MF detection threshold. However, the CNN tends to mis-classify cutouts whose clusters are located near the edge of the cutout, which can be mitigated with staggered cutouts. We leverage the complementarity of the two methods, combining the scores from each method for identification. The purity and completeness of the MF alone are both 0.61, assuming a standard detection threshold. The purity and completeness of the CNN alone are 0.59 and 0.61. The combined classification method yields 0.60 and 0.77, a significant increase for completeness with a modest decrease in purity. We advocate for combined methods that increase the confidence of many lower signal-to-noise clusters.
Recent stellar population analysis of early-type galaxy spectra has demonstrated that the low-mass galaxies in cluster centers have high [$alpha/rm Fe$] and old ages characteristic of massive galaxies and unlike the low-mass galaxy population in the outskirts of clusters and fields. This phenomenon has been termed coordinated assembly to highlight the fact that the building blocks of massive cluster central galaxies are drawn from a special subset of the overall low-mass galaxy population. Here we explore this idea in the IllustrisTNG simulations, particularly the TNG300 run, in order to understand how environment, especially cluster centers, shape the star formation histories of quiescent satellite galaxies in groups and clusters ($M_{200c,z=0}geq10^{13} M_{odot}$). Tracing histories of quenched satellite galaxies with $M_{star,z=0}geq10^{10} M_{odot}$, we find that those in more massive dark matter halos, and located closer to the primary galaxies, are quenched earlier, have shorter star formation timescales, and older stellar ages. The star formation timescale-$M_{star}$ and stellar age-$M_{star}$ scaling relations are in good agreement with observations, and are predicted to vary with halo mass and cluster-centric distance. The dependence on environment arises due to the infall histories of satellite galaxies: galaxies that are located closer to cluster centers in more massive dark matter halos at $z=0$ were accreted earlier on average. The delay between infall and quenching time is shorter for galaxies in more massive halos, and depends on the halo mass at its first accretion, showing that group pre-processing is a crucial aspect in satellite quenching.
The distribution of metals in the intra-cluster medium encodes important information about the enrichment history and formation of galaxy clusters. Here we explore the metal content of clusters in IllustrisTNG - a new suite of galaxy formation simulations building on the Illustris project. Our cluster sample contains 20 objects in TNG100 - a ~(100 Mpc)^3 volume simulation with 2x1820^3 resolution elements, and 370 objects in TNG300 - a ~(300 Mpc)^3 volume simulation with 2x2500^3 resolution elements. The z=0 metallicity profiles agree with observations, and the enrichment history is consistent with observational data going beyond z~1, showing nearly no metallicity evolution. The abundance profiles vary only minimally within the cluster samples, especially in the outskirts with a relative scatter of ~15%. The average metallicity profile flattens towards the center, where we find a logarithmic slope of -0.1 compared to -0.5 in the outskirts. Cool core clusters have more centrally peaked metallicity profiles (~0.8 solar) compared to non-cool core systems (~0.5 solar), similar to observational trends. Si/Fe and O/Fe radial profiles follow positive gradients. The outer abundance profiles do not evolve below z~2, whereas the inner profiles flatten towards z=0. More than ~80% of the metals in the intra-cluster medium have been accreted from the proto-cluster environment, which has been enriched to ~0.1 solar already at z~2. We conclude that the intra-cluster metal distribution is uniform among our cluster sample, nearly time-invariant in the outskirts for more than 10 Gyr, and forms through a universal enrichment history.
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