103 research outputs found
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
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
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
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-
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