Redshift estimation Photometry

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

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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)

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Advantages Disadvantages
Beside, Template-fitting method provide both, z
and T compared to Empirical method that
provides only z
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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
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Typical Results Errors(Csabai et al 2003)
Empirical
Template fit
Hybrid
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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

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Accuracy Improvement

• Generally, we expect improvement when
• Breaking (reduce) degeneracies
• Working with better spectral template
• (more reliable in the UV and more complete)

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This Work
In this work we try to include morphological infor
mation 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
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Morphological Classification (Fukugita et al.
2003)

• For morphological parameter we used parameter T
  visually obtained by Fukugita et al.
• Visual Classification of 2253 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 lt 16 - visual inspection limit
• 1866 galaxies have been spectroscopically
  observed
• 0.001 lt z lt 0.14
• CLASSES
• T 0 1 2 3 4 5 6
• Type E S0 Sa Sb Sc Sd
  Irr

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Properties
Redshift distribution
Petrosian magnitude distribution
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Apparent Sizes vs. Redshift
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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
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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
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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
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• 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)

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Conclusion

• All photometrical parameters from SDSS are
  correlated with
• Fukugitas 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 14 - 15 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