Vince Oliver PhD student of ELTE University Budapest  Assignments within MAGPOP project

Introduction Engagements: Was employed at the AO of Belgrade (Serbia & Mnt) from Solar Physics (long time behavior of Solar abs. spectral lines) - Stellar Physics ( inves. of stellar activity and properties) - Atmospheric turbulence invest. Since Nov have been employed at the ELTE Univ. of Budapest - Redshift estimation using photometry and its application to SDSS

Redshift estimation (Photo-Z) Methods for photo-z estimation can be divided into two basic classes: Empirical method: based on determination of z vs. colors using galaxies with known photometry and spectral redshift (examples: polynomial fitting, artificial neural networks) Template fitting method: based on comparison of simulated and observed colors.

Advantages Empirical method: All effects of dust and evolution are implicit within the z vs. color relation Template fitting method: More physical, beside z we obtain spectral type too

Main limitations Empirical method: Estimates redshift only for the objects that have similar z and spectral type to those in the training set (extrapolation problem) Template fitting method: The template SEDs are poorly known - empirical templates are obtained from observation of local galaxies only, aperture bias, limited wavelength range where the spectra are observed and so on. - model templates are uncertain in the UV

Errors for SDSS DR1 METHOD z spec -z phot Polynomial fit: Repaired empirical templates: (calibration!) Synthetic (BC model) templates: 0.051

Improvements on template fitting We believe that a good choice of initial template spectra obtained by population synthesis model could improve the accuracy of photo-z Positive and negative sides of using population synthesis model + we have a control over the physical parameters of galaxies + no extrapolation problem - continuum properties of models are not perfect yet - too many input parameters to tune Also the use of the morphological information should remove the color degeneracy (close red galaxies vs. high redshift blue ones)

Plans 1. Find the minimal set/combination of relevant model parameters (metallicity, age, dust attenuation params etc.) responsible for broadband optical features, which are used by photo-z. 2.Include morphological prior 3.Apply to SDSS (and other) data and to get redshift, age, metallicity, dust other estimates. 4.To get reliable error bars, confidence limits, on estimated parameters We rely on the experts in this field – Stephan Sharlot, Gian Luigi Granato, Alessandro Bressan and others