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VO-Neural Group G. Longo – P.I. M. Brescia – P.M. Team Corazza ( models )

VO-Neural Group G. Longo – P.I. M. Brescia – P.M. Team Corazza ( models ) O. Laurino (System and models for image segmentation ) S. Cavuoti & E. Russo ( Models – SVM) N. Deniskina ( Grid manager and interfacing with V.O. )

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VO-Neural Group G. Longo – P.I. M. Brescia – P.M. Team Corazza ( models )

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  1. VO-Neural Group • G. Longo – P.I. • M. Brescia – P.M. • Team • Corazza (models) • O. Laurino (System and modelsforimagesegmentation) • S. Cavuoti & E. Russo (Models – SVM) • N. Deniskina (Grid manager and interfacingwithV.O.) • G. d’Angelo (Griddevelopments, JAVA clients and documentation) • M. Garofalo & A. Nocella (UML and models: PPS+NEC) • B. Skordovski & C. Donalek (models: MLP) • S. Cavuoti, E. de Filippis, R. D’Abrusco, (Test & Validation)

  2. The problem Cf. isophotal, petrosian, aperture magnitudesconcentrationindexes, shapeparameters, tc. Band 1 Band 2 Band 3 The scientificexploitationof a multi band, multiepoch (K epochs) surveyimpliestosearchforpatterns, trends, etc. among N points in a DxKdimensionalparameterspace N >109, D>>100, K>10 ….. Band n

  3. The mixed blessing of data richness • Data Mining algorithms scale very badly: • Clustering ~ N log N  N2, ~ D2 • Correlations ~ N log N  N2, ~ Dk (k ≥ 1) • Likelihood, Bayesian ~ Nm(m ≥ 3), ~ Dk (k ≥ 1) Dimensionality reduction (without a significant loss of information) is a critical need! International Virtual Observatory Alliance Started in 2000

  4. Userfriendlyaccesstodistributedcomputing • Transparenthomogeneizationofmultiwavelenghtmultiepochstandards • Similarstandardsforreal and simulated data • Common learningframework • (no needtoadapt know-how’s tospecific data: experimental work focused on science and not on technicalities)

  5. Tasks in Progress • Data MiningModels • MLP (MultilayerPerceptron) FANN librarycompletedbyincluding SOFT-MAX and Cross-Entropy • SVM (SupportVectorMachines) • PPS (ProbabilisticPrincipalSurfaces) • NEC (Negative EntropyClustering & Dendrogram) • Additionalproblems • Star/GalaxyClassification(in coll. withCaltech) • Next (NeuralExtractor) forImageSegmentation and objectparametersextraction • Simulationofcosmicstringssignatures on CosmicMicrowave Background • N body simulations (mesh code) Implementation of interface between ASTROGRID and GRID- SCOPE with different CA

  6. ASTROGRID – GRID Launcher (N. Deniskina) Forms directory on Lupalberto (i.e. executable file, input data) and wraps it Makes connection with SCOPE U.I. (checking certificate) Sends wrapped directory from Lupalberto to Scope U.I. Unzips the wrapped job directory on SCOPE U.I. & forms JDL job Sends job to GRID and waits for the results Wraps the output and sends it to Lupalberto

  7. Chart flow

  8. Scientific cases in progress • Physical classification of galaxies • Search for QSO at intermediate high redshifts • Search for cosmic strings in CMB • Characterization of cosmic large scale structure • http://people.na.infn.it/~astroneural/

  9. First results: AGN classification (Cavuoti, D’Abrusco & D’Angelo) Differentorientations Differentparametersbecomesignificant Differentclusters in parameterspace BUT, STILL THE SAME OBJECT !

  10. First Scientific Experiments on SCOPE GRID SVM on AGN dataset extracted from SDSS for automatic classification of galaxies BoK from spectroscopically confirmed sample SVM code implemented from LIB-SVM 13 parameters for 89.000 objects SVM – RBF needs optimization against 2 parameters (C and g)Maximum of classification rate must be found in a given range 110 grid points in parameter space (each at least 1 h) 110 computers in GRID-SCOPE (Na-CT-CA)

  11. RESULTS: First Experiment Seyfert 1 vs Seyfert 2 Second Experiment AGN vs non-AGN

  12. Thanks to all the other WP’s & a special thank to S. Pardi

  13. The problem: a huge Parameter space R.A d Applications:High dimensionality Massive Data sets (from astronomical survey but also any other high dimensionality data space) t • Anyobserved (simulated) datumpisdefinedby a set ofparameters. Ex.: • RA and dec • time • l • experimentalsetup (spatial and spectralresolution, • limitingmag, limitingsurfacebrightness, etc.) • Polarization • Etc. Lim s.b. l Etc. polarization Lim. Mag. spect. resol time resol. Spatial resol. • The parameterspaceconceptiscrucialto: • Guide the questfornewdiscoveries • Findnewphysicallaws (patterns) in

  14. GRID SCOPE LupalbertoCEC Userinterface resourceregistry ResourceBroker myspace Computationalelement ACRAstrogridMiddleware Output of results Execution of job WorkingNode USER ASTROGRID

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