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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

  • 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)


Vo neural group g longo p i m brescia p m team corazza models

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


Vo neural group g longo p i m brescia p m team corazza models

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


Vo neural group g longo p i m brescia p m team corazza models

  • Userfriendlyaccesstodistributedcomputing

  • Transparenthomogeneizationofmultiwavelenghtmultiepochstandards

  • Similarstandardsforreal and simulated data

  • Common learningframework

  • (no needtoadapt know-how’s tospecific data: experimental work focused on science and not on technicalities)


Vo neural group g longo p i m brescia p m team corazza models

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


Vo neural group g longo p i m brescia p m team corazza models

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



Vo neural group g longo p i m brescia p m team corazza models

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/


Vo neural group g longo p i m brescia p m team corazza models

First results: AGN classification (Cavuoti, D’Abrusco & D’Angelo)

Differentorientations

Differentparametersbecomesignificant

Differentclusters in parameterspace

BUT, STILL THE SAME OBJECT !


Vo neural group g longo p i m brescia p m team corazza models

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)


Vo neural group g longo p i m brescia p m team corazza models

RESULTS:

First Experiment

Seyfert 1 vs Seyfert 2

Second Experiment

AGN vs non-AGN


Vo neural group g longo p i m brescia p m team corazza models

Thanks to all the other WP’s

&

a special thank to S. Pardi


Vo neural group g longo p i m brescia p m team corazza models

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


Vo neural group g longo p i m brescia p m team corazza models

GRID SCOPE

LupalbertoCEC

Userinterface

resourceregistry

ResourceBroker

myspace

Computationalelement

ACRAstrogridMiddleware

Output of results

Execution of job

WorkingNode

USER

ASTROGRID