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-

DAta Mining and Exploration (DAME): New Tools for Knowledge Discovery in Astronomy

The exponential growth of data volumes and complexity in astronomy, as in almost every other field of science, presents both great opportunities and great challenges for an effective knowledge discovery. We describe DAta Mining and Exploration (DAME), a general purpose, Web-based, distributed infrastructure for an effective data mining in massive and complex data sets. DAME includes machine-learning tools such as a variety of Artificial Neural Networks, Support Vector Machines, Self-Organizing Maps, Bayesian Networks, etc., for tasks such as an automated classification or regression fitting in multi-dimensional parameter spaces, etc. DAME also provides workspaces and grid access mechanisms, as well as an extensive documentation and user guides. We illustrate DAME applications on several scientific examples. DAME represents a new generation of astroinformatics tools that will become increasingly important for the data-rich astronomy in the 21st century

Unfinished Business: Putting Your Affairs in Order With Meaning and Purpose

Book Revie

Feature Selection Strategies for Classifying High Dimensional Astronomical Data Sets

The amount of collected data in many scientific fields is increasing, all of them requiring a common task: extract knowledge from massive, multi parametric data sets, as rapidly and efficiently possible. This is especially true in astronomy where synoptic sky surveys are enabling new research frontiers in the time domain astronomy and posing several new object classification challenges in multi dimensional spaces; given the high number of parameters available for each object, feature selection is quickly becoming a crucial task in analyzing astronomical data sets. Using data sets extracted from the ongoing Catalina Real-Time Transient Surveys (CRTS) and the Kepler Mission we illustrate a variety of feature selection strategies used to identify the subsets that give the most information and the results achieved applying these techniques to three major astronomical problems