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تسجيل مستخدم جديدThe detection of globular clusters in galaxies as a data mining problem
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We present an application of self-adaptive supervised learning classifiers derived from the Machine Learning paradigm, to the identification of candidate Globular Clusters in deep, wide-field, single band HST images. Several methods provided by the DAME (Data Mining & Exploration) web application, were tested and compared on the NGC1399 HST data described in Paolillo 2011. The best results were obtained using a Multi Layer Perceptron with Quasi Newton learning rule which achieved a classification accuracy of 98.3%, with a completeness of 97.8% and 1.6% of contamination. An extensive set of experiments revealed that the use of accurate structural parameters (effective radius, central surface brightness) does improve the final result, but only by 5%. It is also shown that the method is capable to retrieve also extreme sources (for instance, very extended objects) which are missed by more traditional approaches.
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In Astrophysics, the identification of candidate Globular Clusters through deep, wide-field, single band HST images, is a typical data analytics problem, where methods based on Machine Learning have revealed a high efficiency and reliability, demonst
rating the capability to improve the traditional approaches. Here we experimented some variants of the known Neural Gas model, exploring both supervised and unsupervised paradigms of Machine Learning, on the classification of Globular Clusters, extracted from the NGC1399 HST data. Main focus of this work was to use a well-tested playground to scientifically validate such kind of models for further extended experiments in astrophysics and using other standard Machine Learning methods (for instance Random Forest and Multi Layer Perceptron neural network) for a comparison of performances in terms of purity and completeness.
We present an end-to-end, two-phase model for the origin of globular clusters (GCs). In the model, populations of stellar clusters form in the high-pressure discs of high-redshift ($z>2$) galaxies (a rapid-disruption phase due to tidal perturbations
from the dense interstellar medium), after which the galaxy mergers associated with hierarchical galaxy formation redistribute the surviving, massive clusters into the galaxy haloes, where they remain until the present day (a slow-disruption phase due to tidal evaporation). The high galaxy merger rates of $z>2$ galaxies allow these clusters to be `liberated into the galaxy haloes before they are disrupted within the high-density discs. This physically-motivated toy model is the first to include the rapid-disruption phase, which is shown to be essential for simultaneously reproducing the wide variety of properties of observed GC systems, such as their universal characteristic mass-scale, the dependence of the specific frequency on metallicity and galaxy mass, the GC system mass-halo mass relation, the constant number of GCs per unit supermassive black hole mass, and the colour bimodality of GC systems. The model predicts that most of these observables were already in place at $z=1$-$2$, although under rare circumstances GCs may still form in present-day galaxies. In addition, the model provides important constraints on models for multiple stellar populations in GCs by putting limits on initial GC masses and the amount of pristine gas accretion. The paper is concluded with a discussion of these and several other predictions and implications, as well as the main open questions in the field.
The properties of globular cluster systems (GCSs) in the core of the nearby galaxy clusters Fornax and Hydra I are presented. In the Fornax cluster we have gathered the largest radial velocity sample of a GCS system so far, which enables us to identi
fy photometric and kinematic sub-populations around the central galaxy NGC 1399. Moreover, ages, metallicities and [alpha/Fe] abundances of a sub-sample of 60 bright globular clusters (GCs) with high S/N spectroscopy show a multi-modal distribution in the correlation space of these three parameters, confirming heterogeneous stellar populations in the halo of NGC 1399. In the Hydra I cluster very blue GCs were identified. They are not uniformly distributed around the central galaxies. 3-color photometry including the U-band reveals that some of them are of intermediate age. Their location coincides with a group of dwarf galaxies under disruption. This is evidence of a structurally young stellar halo still in formation, which is also supported by kinematic measurements of the halo light that point to a kinematically disturbed system. The most massive GCs divide into generally more extended ultra-compact dwarf galaxies (UCDs) and genuine compact GCs. In both clusters, the spatial distribution and kinematics of UCDs are different from those of genuine GCs. Assuming that some UCDs represent nuclei of stripped galaxies, the properties of those UCDs can be used to trace the assembly of nucleated dwarf galaxies into the halos of central cluster galaxies. We show via semi-analytical approaches within a cosmological simulation that only the most massive UCDs in Fornax-like clusters can be explained by stripped nuclei, whereas the majority of lower mass UCDs belong to the star cluster family.
We review the current state of data mining and machine learning in astronomy. Data Mining can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding the potent
ial to fully exploit the exponentially increasing amount of available data, promising great scientific advance. However, if misused, it can be little more than the black-box application of complex computing algorithms that may give little physical insight, and provide questionable results. Here, we give an overview of the entire data mining process, from data collection through to the interpretation of results. We cover common machine learning algorithms, such as artificial neural networks and support vector machines, applications from a broad range of astronomy, emphasizing those where data mining techniques directly resulted in improved science, and important current and future directions, including probability density functions, parallel algorithms, petascale computing, and the time domain. We conclude that, so long as one carefully selects an appropriate algorithm, and is guided by the astronomical problem at hand, data mining can be very much the powerful tool, and not the questionable black box.
Data are presently available on the luminosities and half-light radii of 101 globular clusters associated with low-luminosity parent galaxies. The luminosity distribution of globulars embedded in dwarf galaxies having $M_{v} > -16$ is found to differ
dramatically from that for globular clusters surrounding giant host galaxies with $M_{v} < -16$. The luminosity distribution of globular clusters in giant galaxies peaks at $M_{v} sim -7.5$, whereas that for dwarfs is found to increases monotonically down to the completeness limit of the cluster data at $M_{v} sim -5.0$. Unexpectedly, the power law distribution of the luminosities of globular clusters hosted by dwarf galaxies is seen to be much flatter than the that of bright unevolved part of the luminosity distribution of globular clusters associated with giant galaxies. The specific frequency of globular clusters that are fainter than $M_{v} = -7.5$ is found to be particularly high in dwarf galaxies. The luminosity distribution of the LMC globular clusters is similar to that in giant galaxies, and differs from those of the globulars in dwarf galaxies. The present data appear to show no strong dependence of globular cluster luminosity on the morphological types of their parent galaxies. No attempt is made to explain the unexpected discovery that the luminosity distribution of globular clusters is critically dependent on parent galaxy luminosity (mass?), but insensitive to the morphological type of their host galaxy.
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