Spectroscopy in VO, ESAC Mar 21-23 20071 Access to Spectroscopic Data In the VO Doug Tody...

Spectroscopy in VO, ESAC Mar 21-23 2007 1

Access to Spectroscopic DataIn the VO

Doug Tody (NRAO/US-NVO)for the IVOA DAL working group

INTERNATIONAL VIRTUAL OBSERVATORY ALLIANCE


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


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


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


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


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


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


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


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)


DAL Scope: Types of data (Cambridge 2003)

Dataset

Time Series

Catalog Source Catalog

Event List

Visibility Data

Image NDImage

1D Spectrum

SED

Primary DAL ServicesData Discovery


Dataset

(etc.)

Catalog

Image

Spectrum

TimeSeries

SED

LineList

Simulation

Sloan Spectrum Native data

STIS Spectrum Native data

(etc.)

XMM Spectrum Native data

Spectroscopy in VO, ESAC Mar 21-23 20071 Access to Spectroscopic Data In the VO Doug Tody...

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