Assembly and Classification of Spectral Energy Distributions – A New VO Web Service

1. Assembly and Classification of Spectral Energy Distributions – A New VO Web Service Hans-Martin Adorf, GAVO, Max-Planck-Institut für extraterr. Physik, Garching Florian Kerber, ST-ECF, European Southern Observatory, Garching Gerard Lemson, GAVO, Max-Planck-Institut für extraterr. Physik, Garching Alberto Micol, ST-ECF, European Southern Observatory, Garching Roberto Mignani, European Southern Observatory, Garching Thomas Rauch, Institut für Astronomie und Astrophysik, Universität Tübingen Wolfgang Voges, GAVO, Max-Planck-Institut für extraterr. Physik, Garching

2. Overview • We report progress on a new Web service for automated object classification which comprises four major steps: • An input list of sky-positions is used for querying multiple distributed catalogues covering different wavelength intervals. The sources returned are spatially matched using a probabilistic method. • A list of observed spectral energy distributions (SEDs) is assembled. • The theoretical SEDs are prepared using a library of model spectra. • The obsrvational SEDs are submitted to a classifier that uses the theoretical SED’s for template matching. For each observed SED the three best-matching theoretical SEDs are identified. • A science case has been selected for testing the capabilities of the Web service described. • This work has been carried out as a collaboration between the AVO (http://www.euro-vo.org) and GAVO (http://www.g-vo.org) projects.

3. Scientific Motivation • Many scientific investigations benefit from a multi-spectral (“pan-chromatic”) view of the universe. • This idea has played a vital role at the very beginning of the virtual observatory movement. • Some areas of interest: • “panchromatic mining for quasars” – a key-stone science application of the US-American NVO. • AGN research: start with a list of AGN candidates; collect all photometric data from distributed catalogues covering the full spectral range; classify the AGN-zoo (type I, II, BL Lac, etc.) • planetary nebulae, isolated neutron stars, brown dwarfs, CVs

4. Catalogue Query and Matching • The catalogue query and matching process is itself a three-stage process: • The user uploads an input list of sky-positions • The user selects the catalogues of interest. For each catalogue a deterministic matching service provided by CDS/Vizier is invoked that, for each object in the input list, carries out a simple cone search. • The result is a set of match lists, one per catalogue. Often the matching results are ambiguous. • Finally, the matcher fuses the match-lists into a single master list using GAVO’s “fuzzy” matcher algorithm. • The resulting fused master list contains all plausible match candidates. Each entry in this list contains at most one source from each catalogue.

5. Catalogue Selection

6. Match-List before XMatch

7. Fused Master List (after XMatch)

8. Assembly of the Observational SEDs • The SED-assembly process for the observational data takes several steps: • For each match-candidate the photometric measurements are collected from the contributing catalogues. • Since a given catalogue may not have a matching source, often the photometric measurements are null. Even when the catalogue has a matching source there may still be no photometric measurement in a given passband. • Next, unit conversions are applied to the photometric measurements in order to form a spectral energy distribution (SED). • The resulting (usually incomplete) SEDs make up the “features” which the classifier operates on.

9. Observation Data Preview

10. Preparation of the Theoretical SEDs • For the subsequent classification stage, the theoretical data has to be brought into the observational space. • We have used a grid of stellar model atmosphere spectra (Thomas Rauch, http://astro.uni-tuebingen.de/~rauch/). The theoretical spectra have a much higher resolution than the observational broad band SEDs; the former therefore have been downsampled to match the latter. • In order to match the low spectral resolution of the observations, the theoretical flux was extracted at the central wavelength for each of the 7 wavebands, i.e. Johnson B, V, R, I, H, J, K. (In principle one would have to convert the theoretical spectra using the proper sensitivity curves of the filters.) • No correction was applied for interstellar extinction

11. Library Data Preview

12. Library Data View

13. Supervised Classification • The list of observational SEDs is submitted to a supervised classifier. • The classifier uses the library of theoretical SED’s for template matching. • In principle any user-supplied library may be used; we only require that the uploaded theoretical SEDs comprise the same features as those in the “observed” SEDs. • The SED classifier currently uses a simple deterministic nearest neighbour (NN) algorithm which uses the Euclidean distance in feature space. For each observed SED the NN-classifier identifies the three best-matching theoretical SEDs. • User choices: the features to use in the classification; the method for estimating the scaling factor; the number of best matches to report

14. SED Classifier Central

15. Classification View

16. Quick-look Graphics • For an easy assessment of the results we decided to also provide quick-look on-line graphics. • For each observational SED the chart contains • the “observed” SED and • an overplot of the three best matching theoretical SEDs. • We use the JFreeChart graphics package, wrapped in the Cewolf library for use within JavaServer Pages (JSPs). Fortunately, only a few lines of code are necessary in order to bring up a chart.

17. Quick-look Chart

18. Reporting • Classification results are reported in a classification table. • the ID of the observational SED, • the No of (non-null) features contributing to the classification, and • for each of the best three matches • the ID of the matching theoretical SED, • the dissimilarity between the observed and the matching theoretical SED, and • a scaling factor (the “distance modulus”). • The full complement of pair-wise dissimilarities is also reported. • This table can become very large, since it scales with the number of observational SEDs times the number of theoretical SEDs.

19. Status • The SED classifier is implemented in pure Java • as a standard J2EE Web Application • We successfully use • the JavaServer Faces (JSF) technology, which offers a server- and a client-side state-mechanism, • We extended it by a custom JSF-tag library for table input and output. • an embedded 100% Java database (HSQLDB) for feature selection and reporting, and • the GAVO table utility package (similar to AstroGrid‘s Topcat/STIL package).

20. Conclusions • A proper handling of missing data (null values) is essential for this kind of application. • Quick-look graphics are helpful to let the user assess the classification results. • We need a statistical classifier to adequately handle the photometric uncertainties. • We need to validate the classifier. • This is work-in-progress. We are relying on the CDS/Vizier matching services, which we extend.

21. Selected References • Adorf, H.-M. Classification of Low-Resolution Stellar Spectra via Template Matching -- A Simulation Study. in Workshop "Data Analysis in Astronomy II". 1986. Erice, Italy: Plenum Press, New York, USA. • Kerber F., Mignani R.P., Guglielmetti F., Wicenec A., Galactic Planetary Nebulae and their central stars. I. An accurate and homogeneous set of coordinates.Astron. Astrophys. 408, 1029 (2003) • McGlynn, T.A. , A.A. Suchkov, E.L. Winter, R.J. Hanisch, R.L. White, F. Ochsenbein, S. Derriere, W. Voges, and M.F. Corcoran, Automated Classification of ROSAT Sources Using Heterogeneous Multi-wavelength Source Catalogs, Astrophys. J. (submitted), 2004. • Padovani, P., Allen, M. G., Rosati, P., Walton, N. A. 2004, Discovery of optically faint obscured quasars with Virtual Observatory tools, Astronomy Astrophys. 424, 545. • Rauch, T., Grids of Synthetic Stellar Fluxes. 2004, Thomas Rauch. http://astro.uni-tuebingen.de/~rauch/.