Characterisation Data Model applied to simulated data Mireille Louys, CDS and LSIIT Strasbourg

M.Louys, DM Characterisation for simulated data, Interoperability Victoria, May 2006 Characterisation metadata Should answer the question: Where, when, what, how precise and reliable are the data for one observation? FoV, Bandpass, Resolution, Quantum Efficiency, etc… It is a summary of metadata to be used for data retrieval as in DAL protocols but also for data analysis: resampling, source detections, multi- wavelength analysis, etc…

M.Louys, DM Characterisation for simulated data, Interoperability Victoria, May 2006 Organising metadata Lists the properties of an observation : coverage, resolution, sampling precision, sensitivity, point spread function, transmission curve, etc… –These are the quantitative information that we can derive from the Provenance (acquisition or simulation process). Defines characterisation axes as space, time, wavelength, ‘observable’ which is the measured quantity like flux, photons, counts, etc… Categorise them according to a unified framework → Data model expansion capability

Refvalue LocationRefvalue Location Level of description CoverageResolutionSampling RefValue Bounds Support Variability RefValue Bounds Support Variability Location Bounds Support Sensivity spatial spectral temporal observable Property of the data Physical Axis

UML model: Properties &Levels

M.Louys, DM Characterisation for simulated data, Interoperability Victoria, May 2006 Quality and Errors Each assessed property will have the required value, unit, ucd fields plus an error on this value. The typical error on the data, that is the error we make when we map sampling elements to coordinates, is also needed. –E.g. : astrometric error, photometric error, etc… –They will be attached to the axis on which the mapping is done: spatial, observable, etc… Valid for both systematic and statistical errors.

M.Louys, DM Characterisation for simulated data, Interoperability Victoria, May 2006 Axis description and mapping error

M.Louys, DM Characterisation for simulated data, Interoperability Victoria, May 2006 Categorising use-cases complexity (1) In terms of use cases : Data discovery and selectionlevel Multi regime, multi data type Xmatch of metadata to navigate between complex datasets: cubes, spectra, images, catalogs … → Valid for both observed and simulated data Advanced data processing level 4 –Physical interpretation, recalibration –Description of side products to help for data interpretation: –PSF variation, transmission curve, quality maps, weightmaps, etc… –→ Valid for fine comparison between observed and simulated data

M.Louys, DM Characterisation for simulated data, Interoperability Victoria, May 2006 Characterisation model expansion In terms of data content –More complex axes can be defined: + polarimetry, velocity, visibility –More data properties can be added In terms of dependencies –Coupling of characterisation axes expressed as functions: – e.g. Resolution=f(pos,em,time) expressed as variability maps, e.g. PSF variations maps

MethodsValidation Control params Simulation Process Input Data Interpretation metadata Statistics, Quality Distribution functions Behaviour Limits Evolution models Object structure Atomic Lines Observation Process Interpretation metadata Transmission curves Weighting functions PSF variability Error maps Filters Ambiant conditions Instruments Output Data 3D IFU Spectra 2D Images Cubes Input Data Object of interest Proposal PHENOMENONPROVENANCEDATA CHARACTERISATION Scientific knowledge

MethodsValidation Control params Simulation Process Input Data Interpretation metadata Statistics, Quality Distribution functions Behaviour Limits Evolution models Object structure Atomic Lines Observation Process Interpretation metadata Transmission curves Weighting functions PSF variability Error maps Filters Ambiant conditions Instruments Output Data Input Data Object of interest Proposal PHENOMENON PROVENANCECHARACTERISATION DM Scientific knowledge LEVELS LEVEL 4

M.Louys, DM Characterisation for simulated data, Interoperability Victoria, May 2006 Simulated vs observed data: commonalities and differences They share: axes, data properties, format (?), coding. –spatial axis: observed data are always centered on real sky position, simulated data are not necessarily. Calibration information is extracted from the Provenance information Specific interpretation metadata can be generated by the simulation computation. –Maps, multivariable functions to be described by level4 classes in Characterisation.

M.Louys, DM Characterisation for simulated data, Interoperability Victoria, May 2006 Conclusion The Characterisation model can carry out the description of simulation output data. – Version 1.0 currently to describe the top 3 levels: the footprint of the data on the axes –The next version of the model will emphasize the level 4 structures. Simulation codes to be described – DALIA, Frederic Boone, Obs. Paris, LERMA An homogeneous dynamical interface for various simulation codes + XML schema description –Compatible to workflows in data processing –To be part of the Provenance DM effort? Need for Phenomenon modeling