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Hungarian VO activities

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Synthetic magnitude calculation (many filters) Continuum fitting (Bruzual ... Object types are classified by a set of linear inequlities in magnitude space ... – PowerPoint PPT presentation

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Title: Hungarian VO activities


1
Hungarian VO activities
  • László Dobos, István Csabai
  • Eötvös University
  • Tamás Budavári, Alex Szalay
  • Johns Hopkins

2
Topics
  • Spectrum Service
  • Footprint service
  • Spatial Indexing of large multidimensional
    datasets

3
Spectrum Service
  • Database of over 1M spectraSDSS DR2-4, 2dFGRS,
    theoretical
  • Access remote dataset from the web site or
    central web service
  • Parallel querying of remote databases
  • Intuitive web user interface
  • Web services - several standards

4
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5
Spectrum Service - functions
  • Composite spectrum generation
  • Galactic extinction correction (Schlegel's map)
  • Synthetic magnitude calculation (many filters)
  • Continuum fitting (Bruzual-Charlot models)
  • Line fitting (currently in improvement)

6
Spectrum Services - science
  • Age determination and evolution detection in LRG
    spectra by fitting Bruzual-Charlot models
  • Comparing distributions of theoretical spectrum
    sets with the SDSS spectra
  • We strongly encourage students and PhD students
    to use the VO tools

7
Footprint service
  • Spherical Geometry Library by Tamás Budavári
    (Johns Hopkins)
  • Web user interface
  • database of footprints of large surveys
  • footprint upload
  • intersect, union calculation
  • visualization

8
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9
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10
Multidimensional datasets
  • Typical datasets with 200M points
  • Color Index space - multidimensional
  • gt100GB -gt do not fit into the memory
  • Standard algorithms do not work
  • Same problem with distributed datasets (VO)

11
nD datasets - typical tasks
  • Object types are classified by a set of linear
    inequlities in magnitude space (n dimensional
    polihedra)
  • Compute a histogram of the whole parameter space
  • Find similar objects
  • Find clusters, outliers
  • Compare the distribution of two very large
    datasets

12
nD datasets - problems, idea
  • No random access to the data
  • In a DB server data pages on the disk - takes
    long time to access and load
  • In the VO servers are distributed over the
    world, slow network
  • Main idea organize data on a geometrical basis
    in the n-dimensional space
  • kd-Trees, Voronoi-tessalation

13
Scientific ideas
  • SDSS photometry 5D 300M points finding all
    objects with similar colors source
    classification star quasar separation blue
    red galaxy locus etc.
  • Karhunen-Loeve (PCA) coeffs of Bruzual-Charlot
    models 5-15D 100K-100M p Quick match with
    observed spectra

14
Scientific ideas cont.
  • Magnitudes of spectral synthesis models 5-10D
    100K-100M points match with observations photo-z
    physical props. from photometry check
    consitency of various models (BC-GRASIL)
  • Multiresolution visualization of large number of
    points

15
URLs
  • http//voservices.net
  • http//voservices.net/spectrum
  • http//hvo.elte.hu
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