NVO Summer School, Santa Fe Sept 20081 Access to Spectroscopic Data In the VO Doug Tody (NRAO/US-NVO...
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Transcript of NVO Summer School, Santa Fe Sept 20081 Access to Spectroscopic Data In the VO Doug Tody (NRAO/US-NVO...
NVO Summer School, Santa Fe Sept 2008 1
Access to Spectroscopic DataIn the VO
Doug Tody (NRAO/US-NVO)
INTERNATIONAL VIRTUAL OBSERVATORY ALLIANCE
NVO Summer School, Santa Fe Sept 2008 2
Access to Spectroscopic Data in the VO
NVO Summer School, Santa Fe Sept 2008 3
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
NVO Summer School, Santa Fe Sept 2008 4
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
NVO Summer School, Santa Fe Sept 2008 5
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)
NVO Summer School, Santa Fe Sept 2008 6
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
NVO Summer School, Santa Fe Sept 2008 7
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
NVO Summer School, Santa Fe Sept 2008 8
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)
NVO Summer School, Santa Fe Sept 2008 9
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
NVO Summer School, Santa Fe Sept 2008 10
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
NVO Summer School, Santa Fe Sept 2008 11
Spectrum Data Model
NVO Summer School, Santa Fe Sept 2008 12
Spectrum Data Elements
NVO Summer School, Santa Fe Sept 2008 13
Dataset Characterization
NVO Summer School, Santa Fe Sept 2008 14
NVO Summer School, Santa Fe Sept 2008 15