1. Analysis of Characterisation in Domain Model Context
With application to (SNAP) simulations
Gerard Lemson
DWith feedback from (but don’t blame):
Mireille Louys, Francois Bonnarel
Claudio Gheller, Patrizia Manzato, Laurie Shaw, Herve Wozniak
Miguel Cervino, Igor Chilingarian, Norman Gray, Jaiwon Kim,
Franck Le Petit, Ugo Becciani, Sebastien Derriere
Especially do not blame:
Pat Dowler
IVOA Interop Beijing, DM I

2. IVOA Interop Beijing, DM I
Goal
Understand characterisation ...
context
use
application to (SNAP) simulation data model: beyond space/time/lambda/flux
... through feedback from you
Apply to SNAP
note that use there probably not typical (pattern iso direct reuse?)
Maybe find uses elsewhere?
IVOA Interop Beijing, DM I

3. IVOA Interop Beijing, DM I
Motivation
The thing that is characterised does (did?) not occur explicitly inside characterisation model (Observation is gone)
Found characterisation-like features in SNAP data model, useful for discovery that do contain this thing explicitly
Carries over directly to full domain model
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4. IVOA Interop Beijing, DM I
IVOA Interop Beijing, DM I

5. IVOA Interop Beijing, DM I
IVOA Interop Beijing, DM I

6. IVOA Interop Beijing, DM I
The simulation model
Focuses on experiments, which:
have target objects
which have observables
which have typical values (as function of time)
have representations
consisting of (simulation dependent) object types
which have (simulation dependent) properties/observables (mass, position, wavelength, flux, temperature, entropy etc)
have input parameters
have results
which have collections of measurement (simulation) objects (corresponding to the representation object types)
which assign values (and errors) to the properties
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7. Use values/params for discovery
The full data (results) can not be used as they are in discovery and (SXAP-)queryData
It is hard to query on input parameters when semantics, and consequences not well known/understood
Nevertheless useful info contained in them and desired for querying
Use statistical description characterising the results, both a priori and a posteriori
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8. IVOA Interop Beijing, DM I
In domain
Domain model analyses the domain
a priori characterisation:
restricts possible values an observable may have
summarises effects of input parameters
similar to Characterisation DM (private comm HMcD, ML last year) ??
a posteriori characterisation
summarises actual results
statistics of particular observable in result collection of objects
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9. IVOA Interop Beijing, DM I
IVOA Interop Beijing, DM I

10. IVOA Interop Beijing, DM I
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11. IVOA Interop Beijing, DM I
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12. IVOA Interop Beijing, DM I
Back to simulations
Logical model
application targeted
simpler, less normalised
1 characterisation object
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13. IVOA Interop Beijing, DM I
IVOA Interop Beijing, DM I

14. IVOA Interop Beijing, DM I
Conclusion
Treat characterisation as a pattern iso reusable software/dm component
Coverage characterisation of values
not (yet) of errors (is this Accuracy?)
necessary for discovery and query (of simulations)? No
accuracy
where does this go for simulations
where in domain?
resolution (does this belong on target object, iso representation)
sampling precision (a priori?)
IVOA Interop Beijing, DM I