Spectroscopy in VO, ESAC Mar Access to Spectroscopic Data In the VO Doug Tody (NRAO/US-NVO ) for the IVOA DAL working group I NTERNATIONAL V IRTUAL O BSERVATORY A LLIANCE

Spectroscopy in VO, ESAC Mar Introduction Goals –Brief review of overall VO data access concepts (for newcomers) –Highlight current issues with spectra-related services –Identify issues for possible discussion at this workshop –Discuss SSAP implementations during meeting Agenda –VO data services –Data service functionality –Spectroscopic data services –1-D spectra, SEDs, (TimeSeries), Data cubes

Spectroscopy in VO, ESAC Mar VO Data Services Data Access in the VO –Its all about services ("middleware") –Client application - service/protocol - archival data –Necessary for large scale, multi-wavelength data analysis Access is organized by type of data –Generic dataset (what is common to all data) –Catalog, Image, Spectrum, SED, TimeSeries, Cube, etc. –Data mostly G&EG, but also solar, planetary, theory, etc. Service functionality –Data discovery –Dataset metadata access –Dataset data access

Spectroscopy in VO, ESAC Mar Data Service Functionality Data discovery –The VO Registry is used to find data collections and services –A data service is used to find (discover) individual datasets Dataset metadata access –Dataset metadata may be retrieved without having to get the data –Metadata is uniform, conforming to standard VO data models Dataset identification, curation, target, characterization, etc. –Uniform metadata is essential for automated data selection Dataset data access –Both "archival" and "virtual" (on-the-fly) data are supported Archival data is entire dataset; includes “native” project data –Distributed, multi-wavelength analysis requires virtual data Reduction in data volume: subsetting, filtering, projection Mediation to a standard model - spectral data is heterogeneous Same data can be viewed in different ways

Spectroscopy in VO, ESAC Mar VO Services for Spectroscopic Data Simple Spectral Access (SSA) –"Simple" 1-dimensional spectra –Most survey data is probably of this form 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) Spectral Line Lists (SLAP) –Access to observed and theoretical spectral line lists

Spectroscopy in VO, ESAC Mar Simple Spectral Access (SSA) Summary –Basic concept is a "simple" 1-D spectrum spectral coordinate, flux, error, quality flag, etc. –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. –Data formats VOTable, FITS binary table, CSV, native XML, HTML, etc. Issues –Completion of V1.0 (implementations, interface tweaks) –How to treat multi-segment spectra –Flux units, e.g., absolute flux vs photometric magnitude

Spectroscopy in VO, ESAC Mar

8

9 Spectral Energy Distributions (SEDs) Summary –How to handle SEDs in the VO is still TBD This is a current hot topic; hope to resolve by May IVOA interop –What is a SED? - not as simple as it seems e.g., a (possibly multi-segment) spectrum is a degenerate case –SEDs can be complex Often generated by combining heterogeneous observations Individual observations can be very large SEDs can be dynamically generated A Possible Approach –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 –Pointers given to individual observations Other DAL interfaces used to access complex observations

Spectroscopy in VO, ESAC Mar 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 –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 A Possible Approach –TimeSeries is a primary data object (like Image, Spectrum) –Common spectrophotometric data model –Custom data access interface

Spectroscopy in VO, ESAC Mar 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)

Spectroscopy in VO, ESAC Mar

Spectroscopy in VO, ESAC Mar

Spectroscopy in VO, ESAC Mar DAL Scope: Types of data (Cambridge 2003) Dataset Time Series Catalog Source Catalog Event List Visibility Data Image NDImage 1D Spectrum SED Primary DAL Services Data Discovery

Spectroscopy in VO, ESAC Mar Dataset (etc.) Catalog Image Spectrum TimeSeries SED LineList Simulation Sloan SpectrumNative data STIS SpectrumNative data (etc.) XMM Spectrum Native data