NVO Summer School, Santa Fe Sept 2008 1

Access to Spectroscopic DataIn the VO

Doug Tody (NRAO/US-NVO)

INTERNATIONAL VIRTUAL OBSERVATORY ALLIANCE

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Access to Spectroscopic Data in the VO

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Access to Spectroscopic Data in the VO

• Status– SSA standard completed late 2007

• First of a family of spectrophotometric interfaces• A number of SSA 1.0 services are now coming on line

– Integration of client apps is underway– End-to-end testing and integration needed

• Analysis Tools– Specview, SPLAT, VOSpec, IRAF, TOPCAT, others– Various SED builders under development– Legacy software still needs to become VO-aware

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Types of Spectrophotometric Data

• One Dimensional Spectra– Addressed by Simple Spectral Access (SSA)– Most survey data is probably of this form– Worth treating as a special case

• Spectral Energy Distributions (SEDs)– SEDs are a vital tool for modern astronomical research

• Time Series Data– Not really spectral data; but it is not that simple

• Spectral/Time Data Cubes– A major data product in the future (and present)– Longslit spectra are related

• Spectral Line Lists (SLAP)– Access to observed and theoretical spectral line lists

• Complex Data– Aggregations of simpler datasets, e.g., 1-D spectra

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One Dimensional Spectra (SSA)

• Summary– Basic concept is a "simple" 1-D spectrum

• spectral coordinate, flux, error, quality flag, etc.– SSA includes both a query interface _and_ a spectrum data

model• mediation to a standard model for heterogenous spectra

– Virtual data generation• mediation, cutout, reprojection, dynamic extraction, etc.• TSAP (theory spectra) is a good example

– Data formats• VOTable, FITS binary table, CSV, native XML, HTML, etc.• a good service can return data in any of these formats

• Issues– How to treat multi-segment spectra, e.g., associations– Photometry model needs further work (in progress)

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Spectral Energy Distributions (SEDs)

• SEDs can be complex– Often generated by combining heterogeneous observations– Individual observations can be very large– Source confusion is a real issue– SEDs can (theoretically) be dynamically generated

• Current Concept– A SED is a primary data object (like Image, Spectrum)– Generic dataset metadata describes entire SED object– A uniform view (table) is presented summarizing all segments– Segments are data objects in their own right

• may be included directly in SED dataset, e.g., as resources• large segments may be referenced via an acref URL

• Status– Main effort within NVO is by SAO, NED– Prototype using NED and Chandra data

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Time Series Data

• Summary– Spectrum and TimeSeries are closely related

• both are a series of photometric points• current Spectrum data model almost works for both• SSA has already been used as-is for time series data

– Both can be multi-segment• time series often revisit the same object repeatedly

– Time series can be large, like a highres spectrum• "cutout" capability required, as for Spectrum

• Current Concept– TimeSeries is a primary data object (like Image, Spectrum)– Common spectrophotometric data model– Custom data access interface

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Spectral/Time Data Cubes

• Summary– Data cubes are increasingly common with modern instruments

• radio interferometers, O/IR IFU/MOS instruments– Time cubes (synoptic imagery) are also important

• similar to Spectrum/TimeSeries relationship– Cubes can be very large

• typically 102 MB today, 102 GB not far off– Access required is complex

• subcube, 2-D plane or projection, slice,spectral filter, spectral extraction, etc.

• Possible Approach– Current plan is to extend image interface (SIA) to N-D– Parallels approach of using FITS for radio data cubes– IFU/MOS data may require a different approach (e.g., Euro3D)

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Complex Data

• Problem– How to deal with complex structured datasets

• for example, an Echelle or MOS observation

• Approach– Don't create ever more complex data models– Instead logically associate primary datasets

• SED segments are also an example of this approach– DAL query describes each primary dataset– Association metadata is used to logically associate these

• Advantages– Re-use primary data objects, such as 1-D spectrum– Standard tools can be used to access complex data– Same concept applies elsewhere in DAL; not just for spectra

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Spectrum Data Model

• Motivations– No standard way to represent spectra in astronomy– VO requires automated combination of data from many sources

• need to mediate external data to a standard model• still provide access to native project data as well

• Not just for Spectra– SSA first of second generation DAL interfaces– Generic dataset metadata (DataID, Curation, Target, Char, etc.)– Used for both SSA query and actual spectral datasets

• References– SSA and Spectrum specifications– Spectrum data model spreadsheet– DALServer reference implementation

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Spectrum Data Model

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Spectrum Data Elements

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Dataset Characterization

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