|14h Radioastronomy in the VO - State of the art|
|overall view François Bonnarel Overview|
|Alessandra Zanichelli The Italian radio data archive and the VO perspective|
|Coffee Break 15:30|
|16h Scientist and projects requirements|
|Scientist point of view Katharina Lutz An astronomer's thoughts on radio data and the VO|
|Scientist Yelena Stein Point of view & AENEAS slides|
|Scientist Point of view Marco Iacobelli VO radio interoperability data|
Participants : Mark Allen, Sarah Bertocco, Rosie Bolton (remotely), François Bonnarel, Françoise Genova, Ian Grange (remotely), Marco Iacobelli, Gilles Landais, Mireille Louys, Katharina Lutz, Zheng Meyer-Zhao, Laurent Michel, Marco Molinaro,Carlos Rodrigo, Eric Slezak, Yelena Stein, Arpad Szomoru, Harro Verkouter, Bernd Vollmer, Alessandra Zanichelli
3 main trends in the discussion. Summary by FB with help of notes taken by ML and FG
I ) status and feedback on Multi-D and existing standard protocols for radio data
ObstPAP(Obscore)-SIAV2+DataLink+SODA+clients(Aladin) work for imaging cubes (science ready products) HiPS works for the same (full 3D HiPS or alternatively 2D continuum HiPS + DataLink access to cubes, Other moment maps SODA). See CASDA (ASKAP) data + partially ALMA. Detailed analysis by INAF. MOC also useful for discovery. But MOC in velocity could help. Due to the nature of raw data, image sensitivity may change a lot from pixel to pixel .. --> need for sensitivity cubes? Some of the data are complex. ObsCore not really suited for this. Use of CAOM better? Coarse grain discovery of raw data (or calibrated discovery) via ObstAP/SIAV2 (or HiPS + DataLink) probably possible, but not experimented yet. Links to project native pages for the data can be made available It appears that currently most of the data in the radio archives are not science ready. So there is a big question mark: why and how integrating visibility/raw data in the VO?
II ) Fine grain discovery:
Seems to be needed before retrieving data (due to data volume). Need to define some "data filters" by characterizing data. Visibility characterisation to answer the trustability question: uv coverage = uv ranges, uv power plots, uv distribution maps, amplitude versus spectral frequency, versus phase and versus time. Sensitivity or resolution based discovery. For a given target which are the best observations to provide some minimal sensitivity? some minimal resolution ? It seems to require information on the beam shape and size. Discovery based on configuration details. : number of antennas, array configuration, etc.. or others: filling factor, baseline length ... or PROPOSALS. ---> Provenance dM Trustability by examining different calibration steps (provenance also ?) Aparently some of this stuff can be provided statically, some other stuff can only be generated dynamically according to user needs. This is more difficult to provide for large field of views Open questions: what does the VO can do for this? - integrate additional characterisation feature in existing models (ObsCore) usage of provenance? - Use DataLink to give access to various characterisation or sensitivity features? Including graphically. - provide dynamical tools: as web services? as Desktop applications? - they can probably be easily integrated in the VO as custom services using UWS and DataLink "service descriptor" or applications using SAMP, registry and DAL communications. - provide more may be cumbersome and outside of the scope of the VO. Last but not least : VO is not in charge of verifying the quality of the data. (No data police !). VO Validators deal with compliance of services with the standard.
III ) Data Access for observations (non science ready data)
Among other possibilities:
Web service creating on the fly science ready data (reduction details hidden behind the service interface) Go back to progenitors of science data using provenance and reprocess. characterisation and other metadata information needed to reprocess (see II above) Port code to the data and execute CASA or whatever reduction software remotely. Store and distribute science data produced by users. Reusability of the data. Download data and reduce them with Python VO package tools in Jupyter notebooks. "Measurement sets" seem to be the most complete and widely used data model for visibility data. Used by VLBI. Do we need to tackle other formats (MBFITs, VLBI FITS, PSRFITS for pulsars etc ???) Do we need to map measurements and metadata to some extensions of VO data models (Cube DM, TS DM, Provenance ?) For which purpose? Exposing what is relevant for other users from the internal model ?
IV ) Conclusion.
We need use cases (JIVE,LOFAR) and experience reports (SKA) at all levels. This will allow to find some minimal common requirements. this will allow to go from simple ones to more complex (what can be already done with little changes, what does need changes of larger extent, what is outside of VO Scope). ESCAPE will settle a "Radio astronomy data in the VO" page organised in 3 parts as above which will gather these use cases. JIVE, LOFAR, SKA may have very different use cases.
|Relevant VO standards|
|SIAP 2.0 http://www.ivoa.net/documents/ObsCore/20170509/index.html|
|Cube DM (and dependencies to Dataset DM and STC 2.0 data models) http://www.ivoa.net/documents/NDimCubeDM/20150320/index.html|
|Provenance DM http://www.ivoa.net/documents/ProvenanceDM/20181015/index.html|