NVO Summer School, Santa Fe Sept Access to Spectroscopic Data In the VO Doug Tody (NRAO/US-NVO ) I NTERNATIONAL V IRTUAL O BSERVATORY A LLIANCE

NVO Summer School, Santa Fe Sept Access to Spectroscopic Data in the VO

NVO Summer School, Santa Fe Sept 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 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 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 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 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 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 10 2 MB today, 10 2 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 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 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 Spectrum Data Model

NVO Summer School, Santa Fe Sept Spectrum Data Elements

NVO Summer School, Santa Fe Sept Dataset Characterization

NVO Summer School, Santa Fe Sept

NVO Summer School, Santa Fe Sept