As described in the DR5 paper, the SDSS makes available the results of
two different photometric redshift determinations for galaxies, one based
on neural nets and the other based on a template-fitting approach.
Photometric Redshifts with Neural Nets
The neural net solutions for photometric redshifts and their errors (listed as
Photoz2 in the CAS, and described in
Oyaizu et al. 2008) have
not changed since DR6, and do not use the ubercalibrated magnitudes.
However, we now provide a value-added catalog containing the redshift
probability distribution for each galaxy, p(z), calculated using the
weights method presented in
Cunha et al. (2008).
The p(z) for each
galaxy in the catalog is the weighted distribution of the
spectroscopic redshifts of the 100 nearest training-set galaxies in
the space of dereddened model colors and r magnitude. For the p(z)
calculation we also added the zCOSMOS
(Lilly et al. 2007) and
(Davis et al. 2007)
galaxies to the spectroscopic training
set used for the Photoz2 solution.
Cunha et al. (2008)
showed that summing the p(z) for a sample of
galaxies yields a better estimation of their true redshift
distribution than that of the individual photometric redshifts.
Mandelbaum et al. (2008)
found that this gives significantly smaller
photometric lensing calibration bias than the use of a single photometric redshift
estimate for each galaxy.
Photometric Redshifts: A New Hybrid Technique
With DR7, we have made substantial improvements in the other
photometric redshift code (Photoz), using a hybrid method
that combines the template fitting approach of
Csabai et al. (2003);
i.e., the approach used in DR5 and DR6, and an empirical calibration
using objects with both observed colors and spectroscopic redshifts.
We summarize the method briefly here, with details to follow in a
paper in preparation.
The spectroscopic sample of SDSS contains over 900,000
spectroscopically confirmed galaxies, and the combination of the main
(Strauss et al. 2001),
the LRG sample
(Eisenstein et al. 2001)
and special plates targeted at fainter blue galaxies (DR4 paper) more
or less cover the whole color region in which galaxies lie to the
depths of SDSS. Thus we use the DR7 spectroscopic set as a reference
set for redshift estimation without any additional data from synthetic
The estimation method uses a k-d tree (following
Csabai et al. 2007)
to search in the ubercalibrated u-g, g-r, r-i, i-z color space for the
100 nearest neighbors of every object in the estimation set (i.e. the
galaxies for which we want to estimate redshift) and then estimates
redshift by fitting a local hyperplane to these points, after
rejecting outliers. If an object lies outside the bounding box of the
100 nearest neighbors in color space, the photometric redshift is less
reliable, and the object is flagged accordingly.
We use template fitting to estimate the K-correction, distance
modulus, absolute magnitudes, rest frame colors, and spectral type.
We search for the best match of the measured colors and the synthetic
colors calculated from repaired
(Budavari et al. 2000)
template spectra at the redshift given by the local nearest neighbor
We have found that the mean deviations of the redshifts from the
best-fit hyperplane is a good estimate of the error. That, together
with the flag indicating whether an object lies outside the bounding
box of its neighbors, and the difference between the estimated
photometric redshift and the average redshift of its neighbors, can be
used to select objects with reliable photometric redshift values.
The rms error of the redshift estimation for the reference set
decreases from 0.044 in DR6 to 0.025 in DR7 with this improved
The template-based estimated redshifts versus the true spectroscopic redshifts for a
random sample of 30,000 galaxies with redshifts from SDSS. The
estimated values calculated with the old (DR6) method has
significantly larger scatter and more outliers than the ones with the
new hybrid (DR7) technique. Note that the sample is dominated by red
galaxies (whose photometric redshifts are intrinsically easier to
measure) at z > 0.2.
Iteratively removing the outliers beyond 3 σ gives rms errors of
0.028 and 0.020 for the old and new methods, respectively. In
addition, the reliability of the quoted errors is much higher.
*Text and figures on this page come from an author-created, un-copyedited
version of the SDSS Data Release 7 paper, an article submitted
to Astrophysical Journal Supplements. IOP Publishing Ltd is not responsible for any errors
or omissions in this version of the manuscript or any version derived from it. A preprint of the
DR7 paper is available from the arXiv preprint server.
Last modified: Tue Apr 15 12:00:12 CDT 2003