Photometric redshifts and K-corrections for Sloan Digital Sky Survey Seven Data Release
Ana Laura O'Mill, Fernanda Duplancic, Diego Garc\'ia Lambas, Laerte, Sodr\'e Jr

TL;DR
This paper provides a catalog of galaxy photometric redshifts and k-corrections for SDSS DR7, using neural networks and calibration techniques to enable efficient analysis of galaxy properties up to redshift 0.6.
Contribution
It introduces a new method combining neural networks and calibration for accurate photometric redshifts and k-corrections in SDSS data, optimized for large datasets.
Findings
Photometric redshifts agree with spectroscopic values within rms=0.0227 for MGS.
Redshift distribution matches model predictions up to z=0.6.
Proposed k-correction method is fast and effective for large surveys.
Abstract
We present a catalogue of galaxy photometric redshifts and k-corrections for the Sloan Digital Sky Survey Seven Data Release (SDSS-DR7), available on the World Wide Web. The photometric redshifts were estimated with an artificial neural network using five ugriz bands, concentration indices and Petrosian radii in the g and r bands. We have explored our redshift estimates with different training set concluding that the best choice to improve redshift accuracy comprises the Main Galaxies Sample (MGS), the Luminous Red Galaxies, and galaxies of active galactic nuclei covering the redshift range 0<z<0.3. For the MGS, the photometric redshift estimates agree with the spectroscopic values within rms=0.0227. The derived distribution of photometric redshifts in the range 0<zphot<0.6 agrees well with the model predictions. k-corrections were derived by calibration of the k-correct-v4.2 code…
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