1. 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)
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 Level 1 Coverage Resolution Sampling Spatial (pos)
The basic part of the most general data model: Observation DM
Provides a physical characterisation of a dataset
Level 1
Coverage
Resolution
Sampling
Spatial (pos)
Location
Ref.Value
Temporal (time)
Spectral (em)
Observable (phot)
Level 2
Coverage
Resolution
Sampling
Spatial (pos)
Bounds
Temporal (time)
Spectral (em)
Observable (phot)
Level 3
Coverage
Resolution
Sampling
Spatial (pos)
Support
Temporal (time)
Spectral (em)
Observable (phot)
Level 4
Coverage
Resolution
Sampling
Spatial (pos)
Map
Temporal (time)
Spectral (em)
Observable (phot)

6. Characterisation: relation to STC

7. 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
Characterization[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

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.