ivoa, kyoto may 16-20 20051 simple spectral access ssa query interface doug tody (nrao) markus...

IVOA, Kyoto May 16-20 2005 1

Simple Spectral AccessSSA Query Interface

Doug Tody (NRAO)Markus Dolensky (ESO)

Et. al.

International VIRTUAL OBSERVATORY


Concepts - Dataset-oriented

• Data object type– Spectrum, TimeSeries, SED

• Dataset creation type– Atlas Whole datasets, uniform survey data– Pointed Whole datasets, variable instrumental data– Cutout Subset, data samples are not modified– Resampled Subset, data samples computed by service

• Dataset derivation– Observed An observation– Composite Combination of several observations– Simulated Simulated observation made from real data– Synthetic Data from a theoretical model


Concepts - Role of data model

• Multiple data models– VO spectral data model– “external” data models (project data in archive)

• Active data model mediation– map external data into VO data model– may involve some loss of project-specific information– but this is generally not needed for analysis– enables general multiwavelength analysis

• Separation of data model and data representation– same data object can be represented in multiple ways– multiple output formats (VOTable, FITS, text, etc.)

• Document-oriented vs object API


Compliance

• Minimally compliant service– Implements HTTP GET interface– Input: at least POS, SIZE, FORMAT– Output: all MUST-provide metadata– Support for 1+ compliant output data format (e.g., VOTable)– Service metadata query

• Fully compliant– Input: all SHOULD-provide parameters– Output: all MUST-provide parameters

• Advanced service– Multiple interfaces (GET, WS, ADQL eventually)– Additional optional or service-defined input/output parameters– Can return data in any whatever format the client wants


Basic Usage

• Simple query– POS, SIZE - like cone search– Possibly refined by spectral or time bandpass,

etc.– Most metadata in query response is optional

• Data retrieval– URL-based– Get back a dataset (normally VOTable or FITS)– In simplest case could be wavelength, flux as

text (for Spectrum)– Or external data pass-through


Query Interface

• Mandatory query parameters– POS RA, DEC (ICRS)– SIZE diameter (decimal degrees)– FORMAT VOTable, fits, xml, text, graphics,

html, external

• Should other parameters be mandatory?– e.g., BANDPASS, TIME, SPECRES, APERTURE


Query Interface

• Recommended query parameters– BANDPASS wave1,wave2 (meters in vacuum;

source or observer)– TIME data1,date2 (epoch in decimal years UTC)– APERTURE approx spatial resolution (decimal

degrees)– SPECRES spectral resolution (meters)– TOP number of top-ranked records to return

• Should other parameters be recommended?– e.g., SNR, OBJTYPE, COLLECTION, CREATORID, PUBID


Query Interface

• Optional parameters– OBJTYPE mandatory if service returns multiple

object types– COLLECTION data collection identifier (short name)– CREATORID creator-assigned dataset identifier (at most 1)– PUBID publisher-assigned dataset identifier (at most N)– COMPRESS enable compression (for both data _and_

queries?)

– SNR signal-to-noise ratio– REDSHIFT redshift range (dlambda/lambda)– TARGETCLASS star, galaxy, pulsar, PN, QSO, AGN, etc.


Query Interface

• Service-defined parameters– Service may define additional parameters

• describe in service metadata

– Query-by-Utype has been suggested• probably better to just do ADQL; need expressions

etc.


Query Response

• Format– VOTable– Utype used to identify interface elements

• Includes both protocol and data model

– Standard metadata is in a flat table• Optional extension metadata may be included


Query Response

• Classes of query metadata– Query metadata Describes the query itself– Dataset metadata Describes data object; object-specific– Target metadata Astronomical target– Curation metadata External identification of dataset– Characterization Coverage, Accuracy, Frame, etc.– Instrument metadata Service-defined; hard to

standardize– Access metadata Describes how to access the

dataset


Query Response

• Query Metadata– Query.Score How well object matches

query– Query.LName Logical name (identifier)– Query.LNameKey Logical name key (id-ref)

• Example: LName="MyObj123" LNameKey="server,format"


Query Response

• Dataset Metadata– Dataset.Type Spectrum, TimeSeries, SED, etc.– Dataset.DataModel DM name, e.g., "SSA-V0.90"– Dataset.Title Brief descriptive title of dataset– Dataset.SSA.NSamples Total samples in dataset

Dataset.SSA.Aperture Characteristic aperture diameter– Dataset.SSA.TimeAxis TimeCoord axis (external data)– .SSA.SpectralAxis SpectralCoord axis (external

data)– Dataset.SSA.FluxAxis Flux axis (external data)– Dataset.CreationType atlas, pointed, cutout,

resampled– Dataset.Derivation observed, composite,

simulated, synthetic


Query Response

• Target Metadata– Target.Name Name of astronomical object– Target.Class Target class (star, galaxy, QSO, etc.)– Target.SpectralClass Spectral class (e.g., 'O', 'B', etc.)– Target.Redshift Nominal redshift for object– Derived.VarAmpl Variability amplitude (fraction 0-

1)– Derived.SNR Observed signal to noise ratio


Query Response

• Curation Metadata– Curation.Collection Data collection name (identifier)– Curation.Creator Creator identify (identifier)– Curation.CreatorID Creator-assigned dataset

identifier– Curation.PublisherID Publisher-assigned dataset

identifier– Curation.Date Dataset creation date (ISO date string)– Curation.Version Dataset version (within same

ID)


Query Response

• Characterization1 - Coverage– .Location.Spatial Position (e.g., RA, DEC)– .Location.Time Observation time characteristic value– .Location.Spectral Spectral bandpass characteristic

value– .Location.Spectral.BandID Bandpass ID (band or filter name)– .Bounds.Spatial Aperture footprint (polygon on sky)– .Bounds.Time Low/High time values– .Bounds.Spectral Low/High spectral values– .Bounds.Flux Limiting flux, saturation limit (Jansky)– .Fill.Spatial Spatial sampling filling factor (0-1)– .Fill.Time Time sampling filling factor (0-1)– .Fill.Spectral Spectral sampling filling factor (0-1)


Query Response

• Characterization2 - Accuracy– Accuracy.*.Calibrated uncalibrated, relative,

absolute– Accuracy.*.Resolution Resolution of measured

signal

– Accuracy.*.StatErr Statistical error (measured)

– Accuracy.*.SysErr Systematic error (estimated)

('*' = Spatial, Time, Spectral, Flux)


Query Response

• Characterization3 - Reference Frames– Frame.Spatial.Type Coordinate frame (default ICRS)– Frame.Spatial.Equinox Coordinate system equinox

(J2000)– Frame.Time.System Timescale (TT)– Frame.Time.SIDim SI factor and dimension– Frame.Spectral.SIDim SI factor and dimension– Frame.Flux.SIDim SI factor and dimension– Frame.Flux.UCD UCD of flux value (flux type)

(These apply only to the query response)(SIDim metadata still under construction)


Query Response

• Instrument Metadata– Instrument.Name Instrument name (identifier)– Instrument.Exposure Total exposure time (seconds)– Instrument.<other> Service-defined

• Notes– Optional; provided for instrumental data collections– In general, Collection, Bounds.Time, etc. are preferred– In general Instrument metadata is service-defined– Use Observation model as a starting point


Query Response

• Access Metadata– Access.Reference Data access URL– Access.Format MIME type of returned

dataset– Access.Size Approximate dataset size (bytes)– Access.Server Server endpoint URL

• Staging support goes here in the future– e.g., will dataset access require asynchronous staging– estimated cost to construct dataset


Service Metadata

• Usage– Describe service type and capabilities– Characterize service (data resources served, coverage,

etc.)– Describe interface (optional query parameters)

• Interface– Requires new service metadata query method– Returns resource metadata descriptor (XML)

• Issue:– Registry resource descriptor (XML) or VOTable as in SIA?


SSA/SED Data Model Issues #1

• What is the overall concept?– SSA sees Datasets of several types

• Spectrum, TimeSeries, SED are all top-level Dataset objects• Most metadata in the query response applies to all datasets

– SED Model• SED model sees Spectrum, TimeSeries as special case of

SED• Model a class of spectral data or model SEDs?• Segment is core of spectral (SED) data model


SSA/SED Data Model Issues #2

• Subclassing vs aggregation– Class root is Dataset– Subclass Image, Spectrum, etc.– Aggregate component data models to model each type

dataset

• Component Models– Why [SSA,SED].Segment.Coverage instead of just

Coverage?– Query, Dataset, Curation, Coverage, etc. are components– Can be reused in both SSA query response and in dataset

object

ivoa, kyoto may 16-20 20051 simple spectral access ssa query interface doug tody (nrao) markus...

Documents

query query

data samples

dataset slide

real data synthetic

data representation

identifier query

votable service metadata

data model standard