handling 3d data in the virtual observatory igor chilingarian (cral observatoire de lyon, france/sai...
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Handling 3D Data in the Handling 3D Data in the Virtual ObservatoryVirtual Observatory
Igor ChilingarianIgor Chilingarian (CRAL Observatoire de Lyon, France/SAI MSU, Russia) (CRAL Observatoire de Lyon, France/SAI MSU, Russia)
Francois BonnarelFrancois Bonnarel (CDS Observatoire de Strasbourg, France) (CDS Observatoire de Strasbourg, France)
Mireille LouysMireille Louys (CDS Observatoire de Strasbourg, France) (CDS Observatoire de Strasbourg, France)
Jonathan McDowellJonathan McDowell (Harvard-Smithsonian CfA, USA) (Harvard-Smithsonian CfA, USA)
ADASS XV – San Lorenzo de El Escorial – 4 Oct 2005
I. Chilingarian et al. - ADASS XV - San Lorenzo de El Escorial, 4.Oct.2005I. Chilingarian et al. - ADASS XV - San Lorenzo de El Escorial, 4.Oct.2005
What is 3D spectroscopy (1)?What is 3D spectroscopy (1)?IFU Spectroscopy
I. Chilingarian et al. - ADASS XV - San Lorenzo de El Escorial, 4.Oct.2005I. Chilingarian et al. - ADASS XV - San Lorenzo de El Escorial, 4.Oct.2005
What is 3D spectroscopy (2)?What is 3D spectroscopy (2)?Scanning Fabry-Perot Interferometer
Phase Surface
DataProcessing
I. Chilingarian et al. - ADASS XV - San Lorenzo de El Escorial, 4.Oct.2005I. Chilingarian et al. - ADASS XV - San Lorenzo de El Escorial, 4.Oct.2005
Storing 3D Data in FITSStoring 3D Data in FITS Pure 3D data cube (for IFP data and for some IFU)Pure 3D data cube (for IFP data and for some IFU) 2D-image (one spectrum per row) + binary table 2D-image (one spectrum per row) + binary table
Euro3D Euro3D FormatFormat
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.
I. Chilingarian et al. - ADASS XV - San Lorenzo de El Escorial, 4.Oct.2005I. Chilingarian et al. - ADASS XV - San Lorenzo de El Escorial, 4.Oct.2005
Characterisation DMCharacterisation DM
Level 1Level 1 CoverageCoverage ResolutionResolution SamplingSampling
Spatial (pos)Spatial (pos) LocationLocation Ref.ValueRef.Value Ref.ValueRef.Value
Temporal (time)Temporal (time) LocationLocation Ref.ValueRef.Value Ref.ValueRef.Value
Spectral (em)Spectral (em) LocationLocation Ref.ValueRef.Value Ref.ValueRef.Value
Observable (phot)Observable (phot) LocationLocation Ref.ValueRef.Value Ref.ValueRef.Value
Level 2Level 2 CoverageCoverage ResolutionResolution SamplingSampling
Spatial (pos)Spatial (pos) BoundsBounds BoundsBounds BoundsBounds
Temporal (time)Temporal (time) BoundsBounds BoundsBounds BoundsBounds
Spectral (em)Spectral (em) BoundsBounds BoundsBounds BoundsBounds
Observable (phot)Observable (phot) BoundsBounds BoundsBounds BoundsBounds
Level 3Level 3 CoverageCoverage ResolutionResolution SamplingSampling
Spatial (pos)Spatial (pos) SupportSupport SupportSupport SupportSupport
Temporal (time)Temporal (time) SupportSupport SupportSupport SupportSupport
Spectral (em)Spectral (em) SupportSupport SupportSupport SupportSupport
Observable (phot)Observable (phot) SupportSupport SupportSupport SupportSupport
Level 4Level 4 CoverageCoverage ResolutionResolution SamplingSampling
Spatial (pos)Spatial (pos) MapMap MapMap MapMap
Temporal (time)Temporal (time) MapMap MapMap MapMap
Spectral (em)Spectral (em) MapMap MapMap MapMap
Observable (phot)Observable (phot) MapMap MapMap MapMap
The basic part of the most general data model: Observation DMProvides a physical characterisation of a dataset
I. Chilingarian et al. - ADASS XV - San Lorenzo de El Escorial, 4.Oct.2005I. Chilingarian et al. - ADASS XV - San Lorenzo de El Escorial, 4.Oct.2005
Characterisation: relation to STCCharacterisation: relation to STC
I. Chilingarian et al. - ADASS XV - San Lorenzo de El Escorial, 4.Oct.2005I. Chilingarian et al. - ADASS XV - San Lorenzo de El Escorial, 4.Oct.2005
Characterising IFU datasets (1)Characterising IFU datasets (1)Only first two levels (Location/Ref.Value and Bounds) Only first two levels (Location/Ref.Value and Bounds) should be provided for the whole dataset, because further should be provided for the whole dataset, because further levels become too difficult for understanding.levels become too difficult for understanding.
The rest should be done for each individual IFU spectrumThe 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/LoLimit2VecCharacterization[ucd=pos]/Coverage/Bounds/limits/HiLimit2Vec
Characterization[ucd=time]/Coverage/Location/coord/Time/Value is usually the only temporal-axis-related information preserved by the data processing pipelines
I. Chilingarian et al. - ADASS XV - San Lorenzo de El Escorial, 4.Oct.2005I. Chilingarian et al. - ADASS XV - San Lorenzo de El Escorial, 4.Oct.2005
Characterising IFU datasets (2)Characterising IFU datasets (2)
I. Chilingarian et al. - ADASS XV - San Lorenzo de El Escorial, 4.Oct.2005I. Chilingarian et al. - ADASS XV - San Lorenzo de El Escorial, 4.Oct.2005
Characterising IFU datasets (2)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)
I. Chilingarian et al. - ADASS XV - San Lorenzo de El Escorial, 4.Oct.2005I. Chilingarian et al. - ADASS XV - San Lorenzo de El Escorial, 4.Oct.2005
Characterising IFU datasets (2)Characterising IFU datasets (2)
Characterization[ucd=em]/Coverage/Bounds/limits/LoLimitCharacterization[ucd=em]/Coverage/Bounds/limits/HiLimit
Characterization[ucd=phot]/Coverage/Bounds/limits/LoLimitCharacterization[ucd=phot]/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
I. Chilingarian et al. - ADASS XV - San Lorenzo de El Escorial, 4.Oct.2005I. Chilingarian et al. - ADASS XV - San Lorenzo de El Escorial, 4.Oct.2005
““Live” example: MPFS datasetLive” example: MPFS dataset
I. Chilingarian et al. - ADASS XV - San Lorenzo de El Escorial, 4.Oct.2005I. Chilingarian et al. - ADASS XV - San Lorenzo de El Escorial, 4.Oct.2005
Accessing 3D DataAccessing 3D Data
SSAP 0.9 specifications allow to access any SSAP 0.9 specifications allow to access any type of data described with any data modeltype of data described with any data model
Delivering the 3Delivering the 3rdrd and 4 and 4thth levels of levels of characterisation metadata can be done using characterisation metadata can be done using binary extensions: output XML document will binary extensions: output XML document will contain pointers to binary MIME-attachmentscontain pointers to binary MIME-attachments
Since the 2Since the 2ndnd level is sufficient for most of the level is sufficient for most of the applications, data can be stored in Euro3D applications, data can be stored in Euro3D format within the archiveformat within the archive
I. Chilingarian et al. - ADASS XV - San Lorenzo de El Escorial, 4.Oct.2005I. Chilingarian et al. - ADASS XV - San Lorenzo de El Escorial, 4.Oct.2005
SummarySummary At present 3D data in VO can be described using Characterisaton DMAt present 3D data in VO can be described using Characterisaton DM Latest version of the Simple Spectral Access Protocol provides all the Latest version of the Simple Spectral Access Protocol provides all the
necessary functionality for accessing 3D datanecessary functionality for accessing 3D data Euro3D format developed especially for IFU data can be used as a Euro3D format developed especially for IFU data can be used as a
format for storing such type of data by the datacentersformat for storing such type of data by the datacenters Evenly sampled 3D data cubes (such as IFP, Radio-cubes, etc.) can be 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 characterised as well. But we propose to use pure-3D fits for storing them, mostly because of performance reasonsthem, 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.