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Handling 3D Data in the Virtual Observatory

ADASS XV – San Lorenzo de El Escorial – 4 Oct 2005. Handling 3D Data in the Virtual Observatory. Igor Chilingarian (CRAL Observatoire de Lyon, France/SAI MSU, Russia) Francois Bonnarel (CDS Observatoire de Strasbourg, France) Mireille Louys (CDS Observatoire de Strasbourg, France)

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Handling 3D Data in the Virtual Observatory

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  1. ADASS XV – San Lorenzo de El Escorial – 4 Oct 2005 Handling 3D Data in the Virtual Observatory Igor Chilingarian (CRAL Observatoire de Lyon, France/SAI MSU, Russia) Francois Bonnarel (CDS Observatoire de Strasbourg, France) Mireille Louys (CDS Observatoire de Strasbourg, France) Jonathan McDowell (Harvard-Smithsonian CfA, USA)

  2. What is 3D spectroscopy (1)? IFU Spectroscopy

  3. What is 3D spectroscopy (2)? Scanning Fabry-Perot Interferometer Data Processing Phase Surface

  4. Storing 3D Data in FITS • Pure 3D data cube (for IFP data and for some IFU) • 2D-image (one spectrum per row) + binary table  Euro3D Format • Euro3D Format • FITS binary data table: one row per spectrum • Binary table describing shape of spatial elements (“spaxels”) • Some mandatory metadata, including: common spectral WCS for all spectra, common spatial WCS for all spatial elements, meteo parameters during the observations, etc.

  5. Characterisation DM The basic part of the most general data model: Observation DM Provides a physical characterisation of a dataset

  6. Characterisation: relation to STC

  7. Characterization[ucd=pos]/Coverage/Location/coord/Position2D/Value2Characterization[ucd=pos]/Coverage/Location/coord/Position2D/Value2 Characterization[ucd=pos]/Resolution/ReferenceValue Characterization[ucd=pos]/Sampling/ReferenceValue Characterization[ucd=pos]/Coverage/Bounds/limits/LoLimit2Vec Characterization[ucd=pos]/Coverage/Bounds/limits/HiLimit2Vec Characterising IFU datasets (1) Characterization[ucd=time]/Coverage/Location/coord/Time/Value is usually the only temporal-axis-related information preserved by the data processing pipelines Only first two levels (Location/Ref.Value and Bounds) should be provided for the whole dataset, because further levels become too difficult for understanding. The rest should be done for each individual IFU spectrum

  8. Characterising IFU datasets (2)

  9. Characterising IFU datasets (2) Characterization[ucd=phot]/Coverage/Location/coord/Flux/Value Characterization[ucd=em]/Coverage/Location/coord/Spectral/Value Characterization[ucd=em]/Resolution/ReferenceValue mean spectral resolution (FWHM) Characterization[ucd=em]/Sampling/ReferenceValue  mean sampling (usually constant) Characterization[ucd=phot]/Resolution/ReferenceValue 1 e- (for CCD) Characterization[ucd=phot]/Sampling/ReferenceValue  1 ADU (for CCD)

  10. Characterising IFU datasets (2) Characterization[ucd=phot]/Coverage/Bounds/limits/LoLimit Characterization[ucd=phot]/Coverage/Bounds/limits/HiLimit Characterization[ucd=em]/Coverage/Bounds/limits/LoLimit Characterization[ucd=em]/Coverage/Bounds/limits/HiLimit Characterization[ucd=em]/Resolution/Bounds/limits can be computed using special technics Characterization[ucd=em]/Sampling/Bounds/limits are not defined Characterization[ucd=phot]/Resolution/Bounds/limits are [1e-,1e-] for CCD Characterization[ucd=phot]/Sampling/Bounds/limits are [1e-,1e-] for CCD

  11. “Live” example: MPFS dataset

  12. Accessing 3D Data • SSAP 0.9 specifications allow to access any type of data described with any data model • Delivering the 3rd and 4th levels of characterisation metadata can be done using binary extensions: output XML document will contain pointers to binary MIME-attachments • Since the 2nd level is sufficient for most of the applications, data can be stored in Euro3D format within the archive

  13. Summary • At present 3D data in VO can be described using Characterisaton DM • Latest version of the Simple Spectral Access Protocol provides all the necessary functionality for accessing 3D data • Euro3D format developed especially for IFU data can be used as a format for storing such type of data by the datacenters • Evenly sampled 3D data cubes (such as IFP, Radio-cubes, etc.) can be characterised as well. But we propose to use pure-3D fits for storing them, mostly because of performance reasons All the necessary infrastructural components exist for building VO-compliant science-ready archives of 3D data. During next few months we expect to have a couple of such resources in the Virtual Observatory.

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