1. Eötvös University Budapest in the Network

2.  Seniors: István Csabai (node coordinator): »Photometric redshift estimation, virtual observatories, science database technology, SDSS Zsolt Frei: »Galaxy morphology, galaxy mergers, gravitational waves  Students: Norbert Purger, Bence Kocsis, Merse Gáspár, Márton Trencséni, László Dobos, Dávid Koronczay »Working on SDSS related topics  Network student: Oliver Vince (Belgrade) The Team

3. Focus themes  Development of datamining and visualization techniques – SDSS ‘color space’  Improving photometric redshift estimation  Estimation of physical parameters of galaxies from photometry  Bulge/disk separation of large SDSS galaxies  Virtual Observatory, Spectrum Services

4. Collaboration with other nodes  JHU: Alex Szalay, Tamas Budavari, Ani Thakar … Virtual observatories, SDSS database, photometric redshift estimation Regular visits for seniors ad students  Paris: Stephane Charlot Spectral synthesis models for photo-z, spectral models in VO Oliver Vince visited Paris, and will visit next year New joint topic involving several nodes: „Optical attenuation law of nearby galaxies”

5. u g r i z 300 million points in 5+ dimensions 300 million points in 5+ dimensions Datamining: The Color Space

6. Datamining: Spatial Indexing

7. Datamining: Speed Up Queries

8. Datamining: Visualization Adaptively fetch data from database

9. Datamining:Integration with Database TRADITIONAL APPROACH Flat files, Fortran, C code + Complex manipulation of data - Sequential slow access TRADITIONAL APPROACH Flat files, Fortran, C code + Complex manipulation of data - Sequential slow access SQL DATABASES Oracle, MS SQL Server, … + Organize, efficiently access data - Hard to implement complex algorithms - Multidimensional indexing (OLAP) is limited to categorical data SQL DATABASES Oracle, MS SQL Server, … + Organize, efficiently access data - Hard to implement complex algorithms - Multidimensional indexing (OLAP) is limited to categorical data MULTIDIMENSIONAL INDEXING B-tree, R-tree, K-d tree, BSP-tree … + Many for low D, some for high D + Fast, tuned for various problems - Implemented mostly as memory algorithms, maybe suboptimal in databases MULTIDIMENSIONAL INDEXING B-tree, R-tree, K-d tree, BSP-tree … + Many for low D, some for high D + Fast, tuned for various problems - Implemented mostly as memory algorithms, maybe suboptimal in databases VISUALIZATION Tools using OpenGL, DirectX + Fast - Using files, some tools access database, but not interactive VISUALIZATION Tools using OpenGL, DirectX + Fast - Using files, some tools access database, but not interactive INTEGRATE Implement in SQL Server use for astronomical data-mining and for fast interactive visualization INTEGRATE Implement in SQL Server use for astronomical data-mining and for fast interactive visualization Joint Eötvös & JHU publication at the Conference on Innovative Data Systems Research

10. Photometric redshift estimation Find k nearest neighbors Use polinomial regression Estimate redshift 1M galaxies with known photometry and redshift 100M galaxies with known ugriz photometry, but no redshift ugriz redshift

11.  Joint work between JHU & Eötvös  Photometric redshift calculated for 300M SDSS objects  Included in SDSS DR5 Catalog and Data Release paper  Application: targeting MgII absorbers  collaboration between MPA & Eötvös  network postdoc Vivienne Wild involved Photometric redshift estimation

12. Virtual Observatory: Spectrum & Filter Services  Developed by Eötvös student Laszlo Dobos & JHU researcher Tamas Budavari  Several joint publications  Collaboration with IAP researcher Stephane Charlot to include spectral synthesis models

13. Network events  MAGPOP Virtual Observatory Workshop - Budapest, Hungary, 2005. April 25-26  MAGPOP Summer School - Budapest, Hungary, 2006. August 23-25  Hosting the webpage