The Application of Photometric Redshifts to the SDSS Early Data Release

By István Csabai, Tamás Budavári, Andrew J. Connolly, A. Szalay, Zsuzsanna Győry, N. Benı́tez, James Annis, J. Brinkmann, Daniel J. Eisenstein, M. Fukugita and 5 more. 2002

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The Early Data Release (EDR) from the Sloan Digital Sky Survey provides one of the largest multicolor photometric catalogs currently available to the astronomical community. In this paper we present the first application of photometric redshifts to the ∼6 million extended sources in these data (with 1.8 million sources having r′ < 21). Utilizing a range of photometric redshift techniques, from empirical to template and hybrid techniques, we investigate the statistical and systematic uncertainties present in the redshift estimates for the EDR data. For r′ < 21, we find that the redshift estimates provide realistic redshift histograms with an rms uncertainty in the photometric redshift relation of 0.035 at r′ < 18 and rising to 0.1 at r′ < 21. We conclude by describing how these photometric redshifts and derived quantities, such as spectral type, rest-frame colors, and absolute magnitudes, are stored in the SDSS database. We provide sample queries for searching on photometric redshifts and list the current caveats and issues that should be understood before using these photometric redshifts in statistical analyses of the SDSS galaxies.

Subfields: Astronomy and Astrophysics, Ecology

View Paper: https://www.semanticscholar.org/paper/a0c913bbc8ca17dff309de4cbb19bf10c0aed241

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Citations in the corpus

The Evolving Luminosity Function of Red Galaxies (2006, methodology): “When a sufficiently large training set is available, empirical photometric redshifts are as precise or better than other techniques (e.g., Csabai et al. 2003; Brodwin et al. 2006).”
Galaxy–galaxy weak lensing in the Sloan Digital Sky Survey: intrinsic alignments and shear calibration errors (2004, methodology): “Photometric redshift algorithms using the five SDSS bandpasses (u, g, r, i and z centred at 354, 475, 622, 763 and 905 nm, respectively; Fukugita et al. 1996) have previously been developed (Csabai et al. 2003).”
CFHTLenS: Improving the quality of photometric redshifts with precision photometry (2011, background): “…Baum 1962; Puschell et al. 1982; Koo 1985; Loh & Spillar 1986; Connolly et al. 1995; Koo 1999; Beńıtez 2000; Bolzonella et al. 2000; Wolf et al. 2001; Csabai et al. 2003; Collister & Lahav 2004; Ilbert et al. 2006; Hildebrandt et al. 2008, 2010; Coupon et al. 2009), represent the only practical…”
The Galaxy-Mass Correlation Function Measured from Weak Lensing in the Sloan Digital Sky Survey (2003, methodology): “Although the photometric redshift distributions inferred for SDSS galaxies are consistent with published redshift surveys for r < 21 (Csabai et al. 2003), there are well-known degeneracies in the technique (between type and redshift) that tend to push galaxies to particular values of photo-z.”
The Fourth Data Release of the Sloan Digital Sky Survey (2005, methodology): “The SDSS five-band photometry goes substantially fainter than does the spectroscopy, suggesting the opportunity to derive photometric redshifts for vastly more objects than have spectroscopy (see, e.g., Csabai et al. 2003).”
ANNz: Estimating Photometric Redshifts Using Artificial Neural Networks (2003, methodology): “The method has found successful application to deepfield and wide-field surveys, notably the Hubble Deep Field (e.g. Fernández-Soto, Lanzetta, & Yahil 1999), and the Sloan Digital Sky Survey (Csabai et al. 2003).”
GALAXY CLUSTERS IDENTIFIED FROM THE SDSS DR6 AND THEIR PROPERTIES (2009, methodology): “To obtain an uniform cluster detection in a broad redshift range, we adopt the photometric redshifts by Csabai et al. (2003) in the following cluster detection.”
Dark energy constraints from cosmic shear power spectra: impact of intrinsic alignments on photometric redshift requirements (2007, methodology): “Using real data with approximately four optical observing bands a typical scatter σz is about 0.05 to 0.1 (Csabai et al. 2003; Collister and Lahav 2004; Padmanabhan et al. 2005; Ilbert et al. 2006; Abdalla et al. 2007).”
The Zurich Extragalactic Bayesian Redshift Analyzer and its first application: COSMOS (2006, methodology): “…a result, spectral templates are derived that are a better match to the SEDs of the galaxies in the training set than are the initial model/empirical templates (see also Csabai et al. 2000, Budavári et al. 2001, Beńıtez 2004; Csabai et al. 2003 present an application of this method to SDSS data).”
Photometric redshifts for the SDSS Data Release 12 (2016, methodology): “Additionally, empirical techniques generally perform considerably better than template-based ones within the object type and redshift coverage of the training set (Csabai et al. 2003).”
PHAT: PHoto-z Accuracy Testing (2010, methodology): “Originated from the template-based method described in Csabai et al. (2003), this method uses synthetic colours calculated from the given spectral energy distribution templates.”
Systematic errors in weak lensing: application to SDSS galaxy-galaxy weak lensing (2005, methodology): “…the spectroscopic sample, which is brighter, with much lower photometric errors, supplemented by the CNOC2 spectroscopic redshifts down to r = 21 (Csabai et al. 2003), or used stacked images from the southern SDSS survey due to their lower photometric errors (for KPHOTOZ; M. Blanton, private…”
Galaxy Alignments: Observations and Impact on Cosmology (2015, background): “(2011) studied galaxies at higher redshifts (closer to the typical redshifts of cosmic shear surveys) and were the first to measure large-scale intrinsic alignments in a dataset with photometric redshifts (see Csabai et al. 2003 for a seminal example of the use of photometry for redshifts). The Mega”
A Galaxy Photometric Redshift Catalog for the Sloan Digital Sky Survey Data Release 6 (2007, methodology): “Examples of empirical methods include Polynomial Fitting (Connolly et al. 1995), the Nearest Neighbor method (Csabai et al. 2003), the Nearest Neighbor Polynomial (NNP) technique (Cunha et al. 2007), Artificial Neural Networks (ANN) (Collister & Lahav 2004; Vanzella et al. 2004; d’Abrusco et al.…”