1. 2008-5-20IVOA Interoperability Meeting, Trieste1 Mining data using MATLAB through AstroBox Chao LIU, Chenzhou CUI Presented by: Chenzhou CUI National Astronomical Observatory, China The Chinese V IRTUAL O BSERVATORY

2. 2008-5-20IVOA Interoperability Meeting, Trieste2 China-VO Chinese Virtual Observatory (China-VO) is the national VO project in China initiated in 2002 by Chinese astronomical community led by National Astronomical Observatories, Chinese Academy of Sciences. It focuses its research and development on VO science and applications. R&D focuses: –China-VO Platform –Unified Access to On-line Astronomical Resources and Services –VO-ready Projects and Facilities –VO-based Astronomical Research Activities –VO-based Public Education

3. 2008-5-20IVOA Interoperability Meeting, Trieste3 An active IVOA member IVOA 2007, Beijing 1st Small projects meeting, 2003

4. 2008-5-20IVOA Interoperability Meeting, Trieste4 Our products VOFilter –an XML filter for OpenOffice.org Calc to open VOTable files SkyMouse –A Smart On-line Astronomical Information Collector FitHAS –FITS Header Archiving System VO-DAS –An OGSA-DAI based data access service system to provide unified access to astronomy data, including catalogs, images and spectra. AstroBox –Coming soon –... http://services.china-vo.org

5. 2008-5-20IVOA Interoperability Meeting, Trieste5 First Science Paper from China-VO SDSS DR5 photometric data were searched for new Milky Way companions or substructures in the Galactic halo. Data analysis procedures were based on the VO-DAS. Five candidates are identified as over-dense faint stellar sources that have color-magnitude diagrams similar to those of known globular clusters, or dwarf spherical galaxies. – Liu et al., 2008, A&A)

6. 2008-5-20IVOA Interoperability Meeting, Trieste6 AstroBox: Goals To provide an astronomical data mining application service, supporting VO protocols and tools To provide an network environment for time-consuming astronomical data mining computing A high-level data analysis environment, NOT a raw data analysis tool as IRAF

7. 2008-5-20IVOA Interoperability Meeting, Trieste7 General procedures of data mining Data Accessing –query database –high volume of data Data Pre-processing –select qualified data –eliminate BAD data Data Mining –try multiple times and find a way to get unknown knowledge from specific data set Data Analysis and Interpretation –visualization –comparisons with different data source –associate results with physical meaning

8. 2008-5-20IVOA Interoperability Meeting, Trieste8 An introduction to MATLAB MATLAB is a popular numerical computation software used in variant fields. It provides dozens of toolboxes for different purposes, e.g. statistics, pattern recognizing, optimizing, neural networks etc., as well as a number of way to access data from either local or remote sites. It also offers visualizations by flexible 2D and 3D graphics routines. It supports Java, C, and Fortran as well as its own M-language. It is available of accessing URL resources and parsing XML, which is necessary for embedding web service. In its latest release, refined parallel computation is ready. We conclude that MATLAB is one of the best platforms on which astronomical data mining tools can be developed

9. 2008-5-20IVOA Interoperability Meeting, Trieste9 AstroBox AstroBox is a plug-in package for MATLAB to be used for astronomical computing and data mining It comprises of: –PLASTIC –VOTable –Local DB –VO-DAS client –Astronomical algorithms VO-DAS MATLAB VO-DAS Client MATLAB Database Toolbox Local DB Java Libraries VOTables PLASTIC VO Tools (Aladin, TOPCAT) AstroBox

10. 2008-5-20IVOA Interoperability Meeting, Trieste10 VO utilities in AstroBox VOTable access and conversion –integrate STILS package PLASTIC availability –embed a Java subroutine to connect to PLASTIC Hub through which to exchange data and messages with third party applications, e. g. Aladin and TOPCAT. –SAMP support next... VO-DAS client interface –embed a VO-DAS command line client to send an ADQL to VO-DAS server and wait for query result –It is also capable for asynchronous query, which can access millions of rows of data (on going)

11. 2008-5-20IVOA Interoperability Meeting, Trieste11 Data mining support Regressions –linear regression inherited from MATLAB –nonlinear regression provide astronomical common regressive functions, e.g. King model for density profile of a dwarf galaxy. –kernel regression Fitting –provide specific algorithms for non analytic expression such as complicated observation dataset or user defined functions –several times faster than existed MATLAB functions Spherical surface projecting functions –Equatorial projection & Galactic projection –equal-area Lambert projection in particular for density measurement on spherical surface –Aitoff projection for overall viewing Visualizing functions –2-D plotting –3-D plotting –modified on existed MATLAB functions

12. 2008-5-20IVOA Interoperability Meeting, Trieste12 Other functions High level functions aiming at specific research topics, most of which currently are Milky Way related –Kurucz stellar model –Gerardi stellar population model –isochrone fitting the stellar population –Galactic star count model with disk and halo components –Chemical evolution model for stellar population (on going) Most common used utilities –Monte Carlo methods –coordination transformations –magnitude system transformations

13. 2008-5-20IVOA Interoperability Meeting, Trieste13 Demos 1 PLASTIC implementation

14. 2008-5-20IVOA Interoperability Meeting, Trieste14 Demo 2 Special regression –using a hyperbola relationship between independent and dependent variables Model fitting –density profiles of candidate dwarf galaxy

15. 2008-5-20IVOA Interoperability Meeting, Trieste15 Demo 3 Isochrone fitting –observed data are accessed from either local database or VO- DAS server –query reference data from Gerardi database to fit theoretical isochrones

16. 2008-5-20IVOA Interoperability Meeting, Trieste16 Demo 4 Visualization

17. 2008-5-20IVOA Interoperability Meeting, Trieste17 Demo 5 Parallel computation –fitting a 9-parameter star count model in a 8-core server –faster than that in a single-core computer at a factor of ~8.

18. 2008-5-20IVOA Interoperability Meeting, Trieste18 Future works Release as a tool to the community Extend cosmology methods Establish a distributed parallel computation environment Deploy an on-line data mining service

19. 2008-5-20IVOA Interoperability Meeting, Trieste19 Q & A