KEYWORDS: Data mining, Astronomy, Data modeling, Web 2.0 technologies, Galactic astronomy, Machine learning, Data centers, Spectroscopy, Parallel computing, Data archive systems
The emerging field of AstroInformatics, while on the one hand appears crucial to face the technological challenges, on
the other is opening new exciting perspectives for new astronomical discoveries through the implementation of advanced data mining procedures. The complexity of astronomical data and the variety of scientific problems, however, call for innovative algorithms and methods as well as for an extreme usage of ICT technologies. The DAME (DAta Mining and Exploration) Program exposes a series of web-based services to perform scientific investigation on astronomical massive data sets. The engineering design and requirements, driving its development since the beginning of the project, are projected towards a new paradigm of Web based resources, which reflect the final goal to become a prototype of an efficient data mining framework in the data-centric era.
A multidisciplinary investigation of the Tunguska site (Central Siberia) devastated in 1908 by the explosion of a cosmic body has been carried out in July 14-29, 1999 by the Tunguska99 expedition (see http://www-th.bo.infn.it/tunguska/). In this framework, the remote sensing of a 300-km2 territory has been performed in collaboration with the Russian “State Research Institute of Aviation Systems”(GosNIIAS). An aerophotosurvey and a line scanner survey in 6 spectral bands, from optical to thermal infrared, have been made simultaneously. The 1999 surveys are used to re-examine the 1938 aerophotographic material in order to check details of the 1908 explosion and to verify some recent hypothesis on the event. The 1938 photographic material has been analyzed with the help of the “Tomsk Creative Collective” to obtain new information on the fallen tree distribution. The comparison between the two aerophotosurveys will make it possible to map more accurately the areas with trees surviving the 1908 catastrophe and those with flora variation due to the impact. From the comparison we shall obtain new data on the effects of a cosmic body impact on the forestland coverage, on the spectra reflected from the flora cover, on the Leaf Area Index and other vegetation indices.
Juan Alcala, Mario Radovich, Roberto Silvotti, M. Pannella, M. Arnaboldi, Massimo Capaccioli, E. Puddu, A. Rifatto, G. De Lucia, Amata Mercurio, N. Napolitano, Aniello Grado, Giuseppe Longo, M. Dall'Ora, V. Ripepi, I. Musella, Roberto Scaramella
The Capodimonte Deep Field (OACDF) is a multi-colour imaging survey on two 0.5×0.5 square degree fields performed in the BVRI bands and in six medium-band filters (700 - 900 nm) with the Wide Field Imager (WFI) at the ESO 2.2 m telescope at La Silla, Chile. In this contribution the adopted strategies for the OACDF data reduction are discussed. Preliminary scientific results of the survey are also presented.
KEYWORDS: Lithium, Data mining, Astronomy, Databases, Stereolithography, Data analysis, Digital Light Processing, Palladium, Heads up displays, Observatories
The International Virtual Observatory will pose unprecedented problems to data mining. We shortly discuss the effectiveness of neural networks as aids to the decisional process of the astronomer, and present the AstroMining Package. This package was written in Matlab and C++ and provides an user friendly interactive platform for various data mining tasks. Two applications are also shortly outlined: the derivation of photometric redshifts for a subsample of objects extracted from the Sloan Digital Sky Survey Early Data Release, and the evaluation of systematic patterns in the telemetry data for the Telescopio Nazionale Galilo (TNG).
In this preliminary work on galaxy clustering, we study clusters of arbitrary shape using a 3D galaxy catalog (celestial coordinates and photometric redshifts) derived from Sloan Digital Sky Survey Early Release Data. Spatial influence is modeled using a Markov random field. Comparative model assessment is carried out using an approximation to Bayes factors, viz. the posterior odds of a hypothesis of a given number of clusters versus another. We conclude with a discussion of promising future research directions.
The Italian National "Galileo" Telescope (Telescopio Nazionale "Galileo" - TNG) is a 3.5m telescope located at La Palma, in the Canary islands, which has seen first light in 1998. Available TNG subsystems include four first-generation instruments, plus adaptive optics, meteo and seeing towers; the control and data handling systems are tightly coupled allowing a smooth data flow while preserving integrity. As a part of the data handling systems, the production of a local "Archive at the Telescope" (AaT) is included, and the production of database tables and hard media for the TNG Long-Term Archive (LTA) is supported. The implementation of a LTA prototype has been recently terminated, and the implementation of its operational version is being planned by the Italian National Institute for Astrophysics (INAF).
A description of the AaT and prototype LTA systems are given, including their data handling/archiving and data retrieval capabilities. A discussion of system features and lessons learned is also included, with particular reference to the issues of completeness and data quality. These issues are of particular importance in the perspective of the preparation of a national facility for the archives of data from ground-based telescopes, and its possible inclusion as a data provider in the Virtual Observatory framework.
The advent of large format CCD detectors and of dedicated survey telescopes is providing the astronomical community with datasets of unprecedented size and quality. These data sets cannot be effectively exploited with traditional interactive tools and require the use of innovative data mining and visualization tools resulting from a synergy between astronomy and information sciences. We discuss here some preliminary results obtained by our group in the fields of automatic clustering in multiparametric space and detection of time transients (both astrometric and photometric).
The advent of large format CCD detectors is producing a data flow which cannot be handled with traditional interactive software tools and to be effectively exploited need automatic tools for catalogue extraction and analysis. NExt (Neural Extractor) is a neural network based package capable to perform in a fully automatic way both object detection and star/galaxy classification on large format astronomical images. In this paper we shortly summarize the main aspects of the package stressing some innovative aspect of the procedure implemented to perform the automatic extraction of the data set to be used for the training.
We performed an integrated acoustic and GPR study of the Cheko Lake area (101 degrees E, 62 degrees N) during summer 1999. The GPR study aimed at imaging lake bottom and shallow sedimentary layers to plan coring of sediments coeval with the catastrophic 1908 explosion. The water of the Cheko Lake strongly attenuates radar waves. Therefore, the central and northern sectors of the lake (30 m average depth) were surveyed by means of acoustic techniques only. Integrated acoustic and GPR techniques were used in the shallow southern sector. More than 5 km of radar profiles were obtained in the lake, using 50 MHz and 100 MHz antennas. 150 meters of 200 MHz multi-fold profiles were obtained across the only accessible sectors on land. The GPR profiles processed to date successfully image discontinuities at depths greater than 700 cm. Comparison with acoustic results shows that GPR provides high resolution images of the depth range of interest (0 - 500 cm) which complement the information obtained from sub-bottom profilers and can be calibrated by the gravity cores. A deep (700 cm) flat sub-horizontal reflector, shallow (0 - 200 cm) dipping layers, sigmoidal structures and local chaotic lenses are the primary features imaged by GPR in the lake.
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