IVOA Interop Beijing, DM I 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 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 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 modeldomain model

IVOA Interop Beijing, DM I

IVOA Interop Beijing, DM I

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

IVOA Interop Beijing, DM I 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

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

IVOA Interop Beijing, DM I

IVOA Interop Beijing, DM I

IVOA Interop Beijing, DM I

IVOA Interop Beijing, DM I Back to simulations Logical model –application targeted –simpler, less normalised –1 characterisation object

IVOA Interop Beijing, DM I

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?)