1. Budapest Group Eötvös University MAGPOP kick-off meeting Cassis 2005 January 27-30

2. Cassis meeting 2005 January 27-30 István Csabai 2 HuMuDeS: Hungarian Multi- wavelength Deep Survey 1000 sq degree of the northern hemisphere High resolution spectra from radio through optical to X-ray Volume limited survey up to z=3 Unfortunately does not exist

3. Cassis meeting 2005 January 27-30 István Csabai 3 Astro in Hungary No large telescopes No large grants No postdocs Traditions: solar system, variable stars Background: physics and computer science Links: SDSS/JHU, VO

4. Cassis meeting 2005 January 27-30 István Csabai 4 People István Csabai (photo-z, data mining) Zsolt Frei (morphology, galaxy mergers) László Dobos (student, VO spectrum and filter service) Bence Kocsis (PhD student, galaxy mergers) other PhD students: –Zoltán Lippai (merger tree code) –Merse Gáspár (morphology, disk/bulge separation) –Zsuzsanna Győry (emission line PCA) –Norbert Purger (data mining)

5. Cassis meeting 2005 January 27-30 István Csabai 5 Areas Photometric redshift estimation Galaxy morphology Galaxy evolution: merging Data mining, databases Virtual Observatory

6. Cassis meeting 2005 January 27-30 István Csabai 6 Official MAGPOP tasks 4. Physical properties of galaxies from SDSS potometry –Replace empirical templates with spectral synthesis models (IAP, partly done, need improvements) –Use morphological info in photo-z –Compare spectral type, photo type, morphological type classifications (Nakamura Osamu) –Apply photo-z to SDSS (JHU, done for EDR, still improving the method for new releases) –Compare results from photometric and spectroscopic parameter estimations (MPA,IAP,JHU) – Cross correlate SDSS and GALEX, carry out statistical analysis (JHU) –Study galaxy enviroment, compare properties of high and low mass galaxies (JHU,MPA)

7. Cassis meeting 2005 January 27-30 István Csabai 7 Other plans Apply photo-z for other surveys Galaxy merging, signatures with gravitational waves Develop tools for fast data mining (photometry) VO tools for SED manipulation Release data products for the network (BC03 service, cross matching catalogs)

8. Photometric redshifts MAGPOP kick-off meeting Cassis 2005 January 27-30

9. Cassis meeting 2005 January 27-30 István Csabai 9 Introduction Getting photometry – fast Getting spectra – slow SDSS 80% of time: spectra for the brightest 1% of the objects 20% of time: 5 band photometry for all objects

10. Cassis meeting 2005 January 27-30 István Csabai 10 The idea The same galaxy at different redshifts has different colors Invert the relation: use colors to get the redshift

14. Cassis meeting 2005 January 27-30 István Csabai 14 Methods Empirical –neural network –function fit (polynom) –nearest neighbor –neighbors in a cell + no calibration problem + no input parameters Physical –tempalte fitting maximum likelihood estimation empirical seds model seds + get physical parameters

15. Cassis meeting 2005 January 27-30 István Csabai 15 TEMPLATE LIGHT; SED BROADBAND FILTERS MAGNITUDES, COLORS REDSHIFT OBSERVATIONS

16. Cassis meeting 2005 January 27-30 István Csabai 16 Major limitations Photometric errors Flat spectrum (blue galaxies) Lack of good templates wavelength range bulge/disk evolution repair (deconvolution) model templates: parameter space is too large Spectral type – redshift degeneracy (morphology, priors) Interpolation OK, extrapolation not: need for training sets (SDSS photo-z plates)

17. Cassis meeting 2005 January 27-30 István Csabai 17 The inversion Principal component analysis shows that the ‘spectral space’ is low dimensional (continuum and emission lines also) few colors are enough Cause: small number of major physical parameters If we would use model SED templates, we could get not just redshift, but most of the essential parameters of the galaxies

18. Cassis meeting 2005 January 27-30 István Csabai 18 LIGHT; SED BROADBAND FILTERS MAGNITUDES, COLORS REDSHIFT PARAMETRS age, dust,... GALAXY early type, late type MODEL OBSERVATIONS

20. Cassis meeting 2005 January 27-30 István Csabai 20 Plans Error estimates / confidence Hybrid method: neighbors in a cell interpolation where available – template fitting otherwise Tune photoz with spectral synthesis models (Monte Carlo library of stochastic SFH) -> help to improve SED models Evolution Morphology prior Photo-z web service New surveys (GALEX, CFHTLS) Application: large scale structure

21. Data mining of the color space MAGPOP kick-off meeting Cassis 2005 January 27-30

22. Cassis meeting 2005 January 27-30 István Csabai 22 Introduction Terabytes of data Even current database tecnniques are not efficient enough for handling continous multidimensional spaces Example: given: SED, redshift range (trajectory in color space) need: all nearby objects from a survey

23. Cassis meeting 2005 January 27-30 István Csabai 23 K-d tree Outlier separation PCA tree Voronoi Indexing the color space