1. Photometric
redshift estimation

2. Standard Methods for photometric-redshift estimation
Template-fitting method
Empirical method
Hybrid methods

3. Template-fitting method
Begins with library of template spectra
(empirical or modeled spectra; should cover all spectral type)
Template spectra are corrected for redshift and Galactic extinction
Template colors are calculated
(convolving template spectra and filter function)
Template colors are compared to observed one by minimizing

4. Empirical method Starts with galaxy sample with known zsp and
magnitude m (so called training set)
Relationship zsp = zsp(m) is determined
having m, zph is determined using zsp = zsp(m)
as a calibration curve
example (Connolly et al., 1995)

5. Advantages & Disadvantages
Beside, Template-fitting method provide both, z and T
compared to
Empirical method that provides only z

6. Hybrid methods (Csabai, Budavari)
Takes advantage of positive sides of the Empirical method and
Template Fitting method – use training set to optimize for the shape
of spectral templates to better match the SEDs of the galaxy

7. Typical Results & Errors (Csabai et al 2003)
Empirical
Template
fit
Hybrid

8. Main Sources of uncertainties
Color/redshift, age, dust, morphology degeneracy
Lyman-break/Balmer-break degeneracy for high z
Errors in photometric measurements
Spectral template incompleteness

9. Accuracy Improvement Generally, we expect improvement when:
Breaking (reduce) degeneracies
Working with better spectral template
(more reliable in the UV and more complete)

10. This Work In this work we try to include morphological
information to break the color/morphology degeneracy using Empirical method
Strategy:
1. Find some photometrical parameter (combination) from the SDSS database that best correlates with morphology (morphological parameter)
2. To use it for photometrical redshift estimation improvement

11. Morphological Classification (Fukugita et al. 2003)
For morphological parameter we used parameter T visually obtained by Fukugita et al.
Visual Classification of galaxies using CCD images in g band
3 researchers were involved in visual classification
dispersion T = 0.4 ( T(RC3) = 1. 8 ; photo-plates used )
rp < visual inspection limit
1866 galaxies have been spectroscopically observed
0.001 < z < 0.14
CLASSES T
Type E S0 Sa Sb Sc Sd Irr

12. Properties
Redshift
distribution
Petrosian magnitude
distribution

13. Apparent Sizes vs. Redshift

14. Correlation
We correlate morphological parameter of Fukugita et al. (T) with:
1. All photo parameters from SDSS
2. Photo parameters already used for morph. classification
(concentration index, color index…)
3. Combination of them

15. Illustrative results
Generally, we have non linear dependences between photometrical
rarameters and morphological parameter T (nonlinear regression)
Best correlations are find for CI, colors, fracDev, eClass, tph

16. Results
Results of using photo-parameter that best correlates with morphology in Empirical method for z estimation …
Magnitudes are used in The Empirical Method
Instead of magnitudes, colors are used in the Empirical Method

17. We have also tried to improve photometric redshift estimation by:
Using directly morphological parameter T (~2%)
To increase the number of photometrical parameters in the empirical relation (~4%)
Leaving out photometrical parameters in u and z (larger measurement errors) (~2%)

18. Conclusion All photometrical parameters from SDSS are correlated with
Fukugita’s morphological parameters T:
- CI, colors, fracDev correlate best
Including morphological information in the photometrical
redshift estimation by empirical method improves z up to 4-5 %
(exeption is eClass with % but it is not photometrical parameter)
Living out photometrical parameters with larger measurement error
(u, z bands) slightly improver z.
Using more than one photometrical parameter we have improvement
of several %
next step: Including morphology in Template-fitting method