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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 technological 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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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.
SPLAT-VO is a powerful graphical tool for displaying, comparing, modifying and analyzing astronomical spectra, as well as searching and retrieving spectra from services around the world using Virtual Observatory (VO) protocols and services. The development of SPLAT-VO started in 1999, as part of the Starlink StarJava initiative, sometime before that of the VO, so initial support for the VO was necessarily added once VO standards and services became available. Further developments were supported by the Joint Astronomy Centre, Hawaii until 2009. Since end of 2011 development of SPLAT-VO has been continued by the German Astrophysical Virtual Observatory, and the Astronomical Institute of the Academy of Sciences of the Czech Republic. From this time several new features have been added, including support for the latest VO protocols, along with new visualization and spectra storing capabilities. This paper presents the history of SPLAT-VO, its capabilities, recent additions and future plans, as well as a discussion on the motivations and lessons learned up to now.
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.
Like every other field of intellectual endeavor, astronomy is being revolutionised by the advances in information technology. There is an ongoing exponential growth in the volume, quality, and complexity of astronomical data sets, mainly through large digital sky surveys and archives. The Virtual Observatory (VO) concept represents a scientific and technological framework needed to cope with this data flood. Systematic exploration of the observable parameter spaces, covered by large digital sky surveys spanning a range of wavelengths, will be one of the primary modes of research with a VO. This is where the truly new discoveries will be made, and new insights be gained about the already known astronomical objects and phenomena. We review some of the methodological 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, including differences in basic survey parameters for the federated data sets (e.g., in the positional accuracy and resolution, wavelength coverage, time baseline, etc.), various selection effects, as well as the intrinsic clustering properties (functional form, topology) of the data distributions in the parameter spaces of observed attributes. Answering these challenges will require substantial collaborative efforts and partnerships between astronomers, computer scientists, and statisticians.
In the Virtual Observatory (VO), the Registry provides the mechanism with which users and applications discover and select resources -- typically, data and services -- that are relevant for a particular scientific problem. Even though the VO adopted technologies in particular from the bibliographic community where available, building the Registry system involved a major standardisation effort, involving about a dozen interdependent standard texts. This paper discusses the server-side aspects of the standards and their application, as regards the functional components (registries), the resource records in both format and content, the exchange of resource records between registries (harvesting), as well as the creation and management of the identifiers used in the system based on the notion of authorities. Registry record authors, registry operators or even advanced users thus receive a big picture serving as a guideline through the body of relevant standard texts. To complete this picture, we also mention common usage patterns and open issues as appropriate.
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