اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدThe Virtual Observatory as a Tool to Study Star Cluster Populations in Starburst Galaxies
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The cluster luminosity function (CLF) is one of the most important diagnostics in the study of old globular and young compact star cluster populations. We are currently using ASTROVIRTEL to obtain CLFs in several optical and/or near-infrared passbands, and colour distributions. This will provide us with a powerful analytical tool for the determination of the violent star and cluster formation history of galaxies: we will address questions related to the universality of the globular CLF, the time-scale of low-mass, low-luminosity star cluster depletion and its observability, and environmental effects affecting the shape of the CLFs and the efficiency of the depletion process. This has required the development of complex data mining tools, which are currently being incorporated in ASTROVIRTELs querator.
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We investigate the Coma cluster galaxy luminosity function (GLF) at faint magnitudes, in particular in the u* band by applying photometric redshift techniques applied to deep u*, B, V, R, I images covering a region of ~1deg2 (R 24). Global and local
GLFs in the B, V, R and I bands obtained with photometric redshift selection are consistent with our previous results based on a statistical background subtraction. In the area covered only by the u* image, the GLF was also derived after applying a statistical background subtraction. The GLF in the u* band shows an increase of the faint end slope towards the outer regions of the cluster (from alpha~1 in the cluster center to alpha~2 in the cluster periphery). This could be explained assuming a short burst of star formation in these galaxies when entering the cluster. The analysis of the multicolor type spatial distribution reveals that late type galaxies are distributed in clumps in the cluster outskirts, where X-ray substructures are also detected and where the GLF in the u* band is steeper.
Clusterix 2.0 is a web-based, Virtual Observatory-compliant, interactive tool for the determination of membership probabilities in stellar clusters based on proper motion data using a fully non-parametric method. In the area occupied by the cluster,
the frequency function is made up of two contributions: cluster and field stars. The tool performs an empirical determination of the frequency functions from the Vector-Point Diagram without relying in any previous assumption about their profiles. Clusterix 2.0 allows to search in an interactive way the appropriate spatial areas until an optimal separation of the two populations is obtained. Several parameters can be adjusted to make the calculation computationally feasible without interfering in the quality of the results. The system offers the possibility to query different catalogues, such as Gaia, or upload the user own data. The results of the membership determination can be sent via SAMP to VO tools like TopCat. We apply Clusterix 2.0 to several open clusters with different properties and environments to show the capabilities of the tool: an area of five degrees around NGC 2682 (M 67), an old, well known cluster; a young cluster NGC 2516 with a striking elongate structure extended up to four degrees; NGC 1750 & NGC 1758, a pair of partly overlapping clusters; in the area of NGC 1817 we confirm a little-known cluster, Juchert 23; and in an area with many clusters we disentangle the existence of two overlapping clusters where only one was previously known: Ruprecht 26 and the new, Clusterix 1.
Brightest Cluster Galaxies (BCGs) are mostly elliptical galaxies and very rarely have prominent star formation. We found that five out of 8,812 BCGs are E+A (i.e. post-starburst) galaxies, having the H$delta$~absorption line with an equivalent width
$>2.5AA$ and no distinct emission lines in [O II] and H$alpha$. The E+A features we identified from the BCGs for the first time are not as significant as those in general galaxies, indicating that historically the star formation were not very violent.
Aims. This study focuses on very luminous Mbol<-6.0 mag AGB stars with J-Ks>1.5 mag and H-Ks>0.4 mag in the LMC, SMC, M31, and M33 from 2MASS data. Methods.The data were taken from the 2MASS All-Sky Point Source catalogue archive. We used Virtual O
bservatory tools and took advantage of its capabilities at various stages in the analysis. Results. It is well known that stars with the colors we selected correspond mainly to carbon stars. Although the most luminous AGBs detected here contain a large number of carbon stars,they are not included in existing catalogues produced from data in the optical domain, where they are not visible since they are dust-enshrouded. A comparison of the AGB stars detected with combined near and mid-infrared data from MSX and 2MASS in the LMC shows that 10% of the bright AGB stars are bright carbon stars never detected before and that the other 50% are OH/IR oxygen rich stars, whereas the 40% that remain were not cross-matched. Conclusions. The catalogues of the most luminous AGB stars compiled here are an important complement to existing data. In the LMC, these bright AGB stars are centrally located, whereas they are concentrated in an active star-formation ring in M31. In the SMC and M33, there are not enough of them to draw definite conclusions, although they tend to be centrally located. Their luminosity functions are similar for the four galaxies we studied.
There has been an unprecedented and continuing growth in the volume, quality, and complexity of astronomical data sets over the past few years, mainly through large digital sky surveys. Virtual Observatory (VO) concept represents a scientific and tec
hnological framework needed to cope with this data flood. We review some of the applied statistics and computing challenges posed by the analysis of large and complex data sets expected in the VO-based research. The challenges are driven both by the size and the complexity of the data sets (billions of data vectors in parameter spaces of tens or hundreds of dimensions), by the heterogeneity of the data and measurement errors, the selection effects and censored data, and by the intrinsic clustering properties (functional form, topology) of the data distribution in the parameter space of observed attributes. Examples of scientific questions one may wish to address include: objective determination of the numbers of object classes present in the data, and the membership probabilities for each source; searches for unusual, rare, or even new types of objects and phenomena; discovery of physically interesting multivariate correlations which may be present in some of the clusters; etc.
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