No Arabic abstract
We study the escape rate, dN/dt, from clusters with different radii in a tidal field using analytical predictions and direct N-body simulations. We find that dN/dt depends on the ratio R=r_h/r_j, where r_h is the half-mass radius and r_j the radius of the zero-velocity surface. For R>0.05, the tidal regime, there is almost no dependence of dN/dt on R. To first order this is because the fraction of escapers per relaxation time, t_rh, scales approximately as R^1.5, which cancels out the r_h^1.5 term in t_rh. For R<0.05, the isolated regime, dN/dt scales as R^-1.5. Clusters that start with their initial R, Ri, in the tidal regime dissolve completely in this regime and their t_dis is insensitive to the initial r_h. We predicts that clusters that start with Ri<0.05 always expand to the tidal regime before final dissolution. Their t_dis has a shallower dependence on Ri than what would be expected when t_dis is a constant times t_rh. For realistic values of Ri, the lifetime varies by less than a factor of 1.5 due to changes in Ri. This implies that the survival diagram for globular clusters should allow for more small clusters to survive. We note that with our result it is impossible to explain the universal peaked mass function of globular cluster systems by dynamical evolution from a power-law initial mass function, since the peak will be at lower masses in the outer parts of galaxies. Our results finally show that in the tidal regime t_dis scales as N^0.65/w, with w the angular frequency of the cluster in the host galaxy. [ABRIDGED]
Non-Euclidean geometry with constant negative curvature, i.e., hyperbolic space, has attracted sustained attention in the community of machine learning. Hyperbolic space, owing to its ability to embed hierarchical structures continuously with low distortion, has been applied for learning data with tree-like structures. Hyperbolic Neural Networks (HNNs) that operate directly in hyperbolic space have also been proposed recently to further exploit the potential of hyperbolic representations. While HNNs have achieved better performance than Euclidean neural networks (ENNs) on datasets with implicit hierarchical structure, they still perform poorly on standard classification benchmarks such as CIFAR and ImageNet. The traditional wisdom is that it is critical for the data to respect the hyperbolic geometry when applying HNNs. In this paper, we first conduct an empirical study showing that the inferior performance of HNNs on standard recognition datasets can be attributed to the notorious vanishing gradient problem. We further discovered that this problem stems from the hybrid architecture of HNNs. Our analysis leads to a simple yet effective solution called Feature Clipping, which regularizes the hyperbolic embedding whenever its norm exceeding a given threshold. Our thorough experiments show that the proposed method can successfully avoid the vanishing gradient problem when training HNNs with backpropagation. The improved HNNs are able to achieve comparable performance with ENNs on standard image recognition datasets including MNIST, CIFAR10, CIFAR100 and ImageNet, while demonstrating more adversarial robustness and stronger out-of-distribution detection capability.
We exploit the superb resolution of the new HST/ACS mosaic image of M51 to select a large sample of young (< 1 Gyr) star clusters in the spiral disk, based on their sizes. The image covers the entire spiral disk in B, V, I and H_alpha, at a resolution of 2 pc per pixel. The surface density distribution of 4357 resolved clusters shows that the clusters are more correlated with clouds than with stars, and we find a hint of enhanced cluster formation at the corotation radius. The radius distribution of a sample of 769 clusters with more accurate radii suggests that young star clusters have a preferred effective radius of ~3 pc, which is similar to the preferred radius of the much older GCs. However, in contrast to the GCs, the young clusters in M51 do not show a relation between radius and galactocentric distance. This means that the clusters did not form in tidal equilibrium with their host galaxy, nor that their radius is related to the ambient pressure.
We investigate the rms peculiar velocity of galaxy clusters in the Lambda cold dark matter ($Lambda$CDM) and tau cold dark matter ($tau$CDM) cosmological models using N-body simulations. Cluster velocities for different cluster masses and radii are examined. To identify clusters in the simulations we use two methods: the standard friends-of-friends (FOF) method and the method, where the clusters are defined as the maxima of the density field smoothed on the scale $Rsim 1h^{-1}$ Mpc (DENSMAX). If we use the DENSMAX method, the size of the selected clusters is similar for all clusters. We find that the rms velocity of clusters defined with the DENSMAX method is almost independent of the cluster density and similar to the linear theory expectations. The rms velocity of FOF clusters decreases with the cluster mass and radius. In the $Lambda$CDM model, the rms peculiar velocity of massive clusters with an intercluster separation $d_{cl}=50h^{-1}$ Mpc is $approx$15% smaller than the rms velocity of the clusters with a separation $d_{cl}=10h^{-1}$Mpc.
We present a study of the effective (half-light) radii and other structural properties of a systematically selected sample of young, massive star clusters (YMCs, $geq$$5times10^3$ M$_{odot}$ and $leq$200 Myr) in two nearby spiral galaxies, NGC 628 and NGC 1313. We use Hubble Space Telescope WFC3/UVIS and archival ACS/WFC data obtained by the Legacy Extragalactic UV Survey (LEGUS), an HST Treasury Program. We measure effective radii with GALFIT, a two-dimensional image-fitting package, and with a new technique to estimate effective radii from the concentration index (CI) of observed clusters. The distribution of effective radii from both techniques spans $sim$0.5-10 pc and peaks at 2-3 pc for both galaxies. We find slight positive correlations between effective radius and cluster age in both galaxies, but no significant relationship between effective radius and galactocentric distance. Clusters in NGC 1313 display a mild increase in effective radius with cluster mass, but the trend disappears when the sample is divided into age bins. We show that the vast majority of the clusters in both galaxies are much older than their dynamical times, suggesting they are gravitationally bound objects. We find that about half of the clusters in NGC 628 are underfilling their Roche lobes, based on their Jacobi radii. Our results suggest that the young, massive clusters in NGC 628 and NGC 1313 are expanding due to stellar mass loss or two-body relaxation and are not significantly influenced by the tidal fields of their host galaxies.
We identify structures of the young star cluster NGC 2232 in the solar neighborhood (323.0 pc), and a newly discovered star cluster LP 2439 (289.1 pc). Member candidates are identified using the Gaia DR2 sky position, parallax and proper motion data, by an unsupervised machine learning method, textsc{StarGO}. Member contamination from the Galactic disk is further removed using the color magnitude diagram. The four identified groups (NGC 2232, LP 2439 and two filamentary structures) of stars are coeval with an age of 25 Myr and were likely formed in the same giant molecular cloud. We correct the distance asymmetry from the parallax error with a Bayesian method. The 3D morphology shows the two spherical distributions of clusters NGC 2232 and LP 2439. Two filamentary structures are spatially and kinematically connected to NGC 2232. Both NGC 2232 and LP 2439 are expanding. The expansion is more significant in LP 2439, generating a loose spatial distribution with shallow volume number and mass density profiles. The expansion is suggested to be mainly driven by gas expulsion. NGC 2232, with 73~percent of the cluster mass bound, is currently experiencing a process of re-virialization, However, LP 2439, with 52 percent cluster mass being unbound, may fully dissolve in the near future. The different survivability traces different dynamical states of NGC 2232 and LP 2439 prior to the onset of gas expulsion. NGC 2232 may have been substructured and subvirial, while LP 2439 may either have been virial/supervirial, or it has experienced a much faster rate of gas removal.