Astrophysics in S.Co.P.E

S.Co.P.E. is one of the four projects funded by the Italian Government in order to provide Southern Italy with a distributed computing infrastructure for fundamental science. Beside being aimed at building the infrastructure, S.Co.P.E. is also actively pursuing research in several areas among which astrophysics and observational cosmology. We shortly summarize the most significant results obtained in the first two years of the project and related to the development of middleware and Data Mining tools for the Virtual Observatory

GRID-Launcher v.1.0

GRID-launcher-1.0 was built within the VO-Tech framework, as a software interface between the UK-ASTROGRID and a generic GRID infrastructures in order to allow any ASTROGRID user to launch on the GRID computing intensive tasks from the ASTROGRID Workbench or Desktop. Even though of general application, so far the Grid-Launcher has been tested on a few selected softwares (VONeural-MLP, VONeural-SVM, Sextractor and SWARP) and on the SCOPE-GRID

DAME: A Distributed Web based Framework for Knowledge Discovery in Databases

Massive data sets explored in many e-science communities, as in the Astrophysics case, are gathered by a very large number of techniques and stored in very diversified and often-incompatible data repositories. Moreover, we need to integrate services across distributed, heterogeneous, dynamic virtual organizations formed from the different resources within a single enterprise and/or from external resource sharing and service provider relationships. The DAME project aims at creating a distributed e-infrastructure to guarantee integrated and asynchronous access to data collected by very different experiments and scientific communities in order to correlate them and improve their scientific usability. The project consists of a data mining framework with powerful software instruments capable to work on massive data sets, organized by following Virtual Observatory standards, in a distributed computing environment. The integration process can be technically challenging because of the need to achieve a specific quality of service when running on top of different native platforms. In these terms, the result of the DAME project effort is a service-oriented architecture, by using appropriate standards and incorporating Cloud/Grid paradigms andWeb services, that will have as main target the integration of interdisciplinary distributed systems within and across organizational domains

The VO-Neural project: recent developments and some applications

VO-Neural is the natural evolution of the Astroneural project which was started in 1994 with the aim to implement a suite of neural tools for data mining in astronomical massive data sets. At a difference with its ancestor, which was implemented under Matlab, VO-Neural is written in C++, object oriented, and it is specifically tailored to work in distributed computing architectures. We discuss the current status of implementation of VO-Neural, present an application to the classification of Active Galactic Nuclei, and outline the ongoing work to improve the functionalities of the package.Comment: Contributed, Data Centre Alliance Workshops: GRID and the Virtual Observatory, April 9-11 Munich, to appear in Mem. SAI

CLaSPS: a new methodology for Knowledge extraction from complex astronomical dataset

In this paper we present the Clustering-Labels-Score Patterns Spotter (CLaSPS), a new methodology for the determination of correlations among astronomical observables in complex datasets, based on the application of distinct unsupervised clustering techniques. The novelty in CLaSPS is the criterion used for the selection of the optimal clusterings, based on a quantitative measure of the degree of correlation between the cluster memberships and the distribution of a set of observables, the labels, not employed for the clustering. In this paper we discuss the applications of CLaSPS to two simple astronomical datasets, both composed of extragalactic sources with photometric observations at different wavelengths from large area surveys. The first dataset, CSC+, is composed of optical quasars spectroscopically selected in the SDSS data, observed in the X-rays by Chandra and with multi-wavelength observations in the near-infrared, optical and ultraviolet spectral intervals. One of the results of the application of CLaSPS to the CSC+ is the re-identification of a well-known correlation between the alphaOX parameter and the near ultraviolet color, in a subset of CSC+ sources with relatively small values of the near-ultraviolet colors. The other dataset consists of a sample of blazars for which photometric observations in the optical, mid and near infrared are available, complemented for a subset of the sources, by Fermi gamma-ray data. The main results of the application of CLaSPS to such datasets have been the discovery of a strong correlation between the multi-wavelength color distribution of blazars and their optical spectral classification in BL Lacs and Flat Spectrum Radio Quasars and a peculiar pattern followed by blazars in the WISE mid-infrared colors space. This pattern and its physical interpretation have been discussed in details in other papers by one of the authors.Comment: 18 pages, 9 figures, accepted for publication in Ap

Data Deluge in Astrophysics: Photometric Redshifts as a Template Use Case

Astronomy has entered the big data era and Machine Learning based methods have found widespread use in a large variety of astronomical applications. This is demonstrated by the recent huge increase in the number of publications making use of this new approach. The usage of machine learning methods, however is still far from trivial and many problems still need to be solved. Using the evaluation of photometric redshifts as a case study, we outline the main problems and some ongoing efforts to solve them.Comment: 13 pages, 3 figures, Springer's Communications in Computer and Information Science (CCIS), Vol. 82

A web application for photometric redshift estimation

In the era of massive astronomical datasets, efficient identification of candidate quasars and the reconstruction of their three dimensional distribution in the Universe is a key requirement for constraining some of the main issues regarding the formation and evolution of QSOs. A method for the determination of photometric redshifts of QSOs based on multiwavelength photometry and on a combination of data mining techniques will be discussed. This procedure, specifically suited for accompanying the candidate selection method discussed in (D’Abrusco et al. 2008), makes use of specific tools developed under the EuroVO and NVO frameworks for data gathering, pre-processing and mining, while relying on the scaling capabilities of the computing grid. This method allowed us to obtain photometric redshifts with an increased accuracy (up to 30%) with respect to the literature

The DAME/VO-Neural Infrastructure: an Integrated Data Mining System Support for the Science Community

Astronomical data are gathered through a very large number of heterogeneous techniques and stored in very diversified and often incompatible data repositories. Moreover in the e-science environment, it is needed to integrate services across distributed, heterogeneous, dynamic "virtual organizations" formed by different resources within a single enterprise and/or external resource sharing and service provider relationships. The DAME/VONeural project, run jointly by the University Federico II, INAF (National Institute of Astrophysics) Astronomical Observatories of Napoli and the California Institute of Technology, aims at creating a single, sustainable, distributed e-infrastructure for data mining and exploration in massive data sets, to be offered to the astronomical (but not only) community as a web application. The framework makes use of distributed computing environments (e.g. S.Co.P.E.) and matches the international IVOA standards and requirements. The integration process is technically challenging due to the need of achieving a specific quality of service when running on top of different native platforms. In these terms, the result of the DAME/VO-Neural project effort will be a service-oriented architecture, obtained by using appropriate standards and incorporating Grid paradigms and restful Web services frameworks where needed, that will have as main target the integration of interdisciplinary distributed systems within and across organizational domains.Comment: 10 pages, Proceedings of the Final Workshop of the Grid Projects of the Italian National Operational Programme 2000-2006 Call 1575; Edited by Cometa Consortium, 2009, ISBN: 978-88-95892-02-