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We measure photometric redshifts and spectral types for galaxies in the COSMOS survey. We use template fitting technique combined with luminosity function priors and with the option to simultaneously estimate dust extinction (i.e. E(B-V)) for each galaxy.Our estimated redshifts are accurate to i<25 and z~1.2. Using simulations with sampling and noise characteristics similar to those in COSMOS, the accuracy and reliability is estimated for the photometric redshifts as a function of the magnitude limits of the sample, S/N ratios and the number of bands used. From the simulations we find that the ratio of derived 95% confidence interval in the redshift probability distribution to the estimated photometric redshift (D95) can be used to identify and exclude the catastrophic failures in the photometric redshift estimates. We compare the derived redshifts with high-reliability spectroscopic redshifts for a sample of 868 normal galaxies with z < 1.2 from zCOSMOS. Considering different scenarios, depending on using prior, no prior and/or extinction, we compare the photometric and spectroscopic redshifts for this sample. This corresponds to an rms scatter of 0.031, with a small number of outliers (<2.5%). We also find good agreement (rms=0.10) between photometric and spectroscopic redshifts for Type II AGNs. We compare results from our photometric redshift procedure with three other independent codes and find them in excellent agreement. We show preliminary results, based on photometric redshifts for the entire COSMOS sample (to i < 25 mag.).
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We present accurate photometric redshifts in the 2-deg2 COSMOS field. The redshifts are computed with 30 broad, intermediate, and narrow bands covering the UV (GALEX), Visible-NIR (Subaru, CFHT, UKIRT and NOAO) and mid-IR (Spitzer/IRAC). A chi2 template-fitting method (Le Phare) was used and calibrated with large spectroscopic samples from VLT-VIMOS and Keck-DEIMOS. We develop and implement a new method which accounts for the contributions from emission lines (OII, Hbeta, Halpha and Ly) to the spectral energy distributions (SEDs). The treatment of emission lines improves the photo-z accuracy by a factor of 2.5. Comparison of the derived photo-z with 4148 spectroscopic redshifts (i.e. Delta z = zs - zp) indicates a dispersion of sigma_{Delta z/(1+zs)}=0.007 at i<22.5, a factor of 2-6 times more accurate than earlier photo-z in the COSMOS, CFHTLS and COMBO-17 survey fields. At fainter magnitudes i<24 and z<1.25, the accuracy is sigma_{Delta z/(1+zs)}=0.012. The deep NIR and IRAC coverage enables the photo-z to be extended to z~2 albeit with a lower accuracy (sigma_{Delta z/(1+zs)}=0.06 at i~24). The redshift distribution of large magnitude-selected samples is derived and the median redshift is found to range from z=0.66 at 22<i<22.5 to z=1.06 at 24.5<i<25. At i<26.0, the multi-wavelength COSMOS catalog includes approximately 607,617 objects. The COSMOS-30 photo-z enable the full exploitation of this survey for studies of galaxy and large scale structure evolution at high redshift.
We use extensive multi-wavelength photometric data from the Great Observatories Origins Deep Survey (GOODS) to estimate photometric redshifts for a sample of 434 galaxies with spectroscopic redshifts in the Chandra Deep Field South. Using the Bayesian method, which incorporates redshift/magnitude priors, we estimate photometric redshifts for galaxies in the range 18 < R (AB) < 25.5, giving an rms scatter of 0.11. The outlier fraction is < 10%, with the outlier-clipped rms being 0.047. We examine the accuracy of photometric redshifts for several, special sub--classes of objects. The results for extremely red objects are more accurate than those for the sample as a whole, with rms of 0.051 and very few outliers (3%). Photometric redshifts for active galaxies, identified from their X-ray emission, have a dispersion of 0.104, with 10% outlier fraction, similar to that for normal galaxies. Employing a redshift/magnitude prior in this process seems to be crucial in improving the agreement between photometric and spectroscopic redshifts.
We apply clustering-based redshift inference to all extended sources from the Sloan Digital Sky Survey photometric catalogue, down to magnitude r = 22. We map the relationships between colours and redshift, without assumption of the sources spectral energy distributions (SED). We identify and locate star-forming, quiescent galaxies, and AGN, as well as colour changes due to spectral features, such as the 4000 AA{} break, redshifting through specific filters. Our mapping is globally in good agreement with colour-redshift tracks computed with SED templates, but reveals informative differences, such as the need for a lower fraction of M-type stars in certain templates. We compare our clustering-redshift estimates to photometric redshifts and find these two independent estimators to be in good agreement at each limiting magnitude considered. Finally, we present the global clustering-redshift distribution of all Sloan extended sources, showing objects up to z ~ 0.8. While the overall shape agrees with that inferred from photometric redshifts, the clustering redshift technique results in a smoother distribution, with no indication of structure in redshift space suggested by the photometric redshift estimates (likely artifacts imprinted by their spectroscopic training set). We also infer a higher fraction of high redshift objects. The mapping between the four observed colours and redshift can be used to estimate the redshift probability distribution function of individual galaxies. This work is an initial step towards producing a general mapping between redshift and all available observables in the photometric space, including brightness, size, concentration, and ellipticity.
In this work I discuss the necessary steps for deriving photometric redshifts for luminous red galaxies (LRGs) and galaxy clusters through simple empirical methods. The data used is from the Sloan Digital Sky Survey (SDSS). I show that with three bands only ({it gri}) it is possible to achieve results as accurate as the ones obtained by other techniques, generally based on more filters. In particular, the use of the $(g-i)$ color helps improving the final redshifts (especially for clusters), as this color monotonically increases up to $z sim 0.8$. For the LRGs I generate a catalog of $sim 1.5$ million objects at $z < 0.70$. The accuracy of this catalog is $sigma = 0.027$ for $z le 0.55$ and $sigma = 0.049$ for $0.55 < z le 0.70$. The photometric redshift technique employed for clusters is independent of a cluster selection algorithm. Thus, it can be applied to systems selected by any method or wavelength, as long as the proper optical photometry is available. When comparing the redshift listed in literature to the photometric estimate, the accuracy achieved for clusters is $sigma = 0.024$ for $z le 0.30$ and $sigma = 0.037$ for $030 < z le 0.55$. However, when considering the spectroscopic redshift as the mean value of SDSS galaxies on each cluster region, the accuracy is at the same level as found by other authors: $sigma = 0.011$ for $z le 0.30$ and $sigma = 0.016$ for $030 < z le 0.55$. The photometric redshift relation derived here is applied to thousands of cluster candidates selected elsewhere. I have also used galaxy photometric redshifts available in SDSS to identify groups in redshift space and then compare the redshift peak of the nearest group to each cluster redshift (ABRIDGED).
Upcoming imaging surveys, such as LSST, will provide an unprecedented view of the Universe, but with limited resolution along the line-of-sight. Common ways to increase resolution in the third dimension, and reduce misclassifications, include observing a wider wavelength range and/or combining the broad-band imaging with higher spectral resolution data. The challenge with these approaches is matching the depth of these ancillary data with the original imaging survey. However, while a full 3D map is required for some science, there are many situations where only the statistical distribution of objects (dN/dz) in the line-of-sight direction is needed. In such situations, there is no need to measure the fluxes of individual objects in all of the surveys. Rather a stacking procedure can be used to perform an `ensemble photo-z. We show how a shallow, higher spectral resolution survey can be used to measure dN/dz for stacks of galaxies which coincide in a deeper, lower resolution survey. The galaxies in the deeper survey do not even need to appear individually in the shallow survey. We give a toy model example to illustrate tradeoffs and considerations for applying this method. This approach will allow deep imaging surveys to leverage the high resolution of spectroscopic and narrow/medium band surveys underway, even when the latter do not have the same reach to high redshift.
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