A Galaxy Photometric Redshift Catalog for the Sloan Digital Sky Survey Data Release 6

By H. Oyaizu, Marcos Lima, C. Cunha, Huan Lin, Joshua A. Frieman, Erin Sheldon. 2007

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We present and describe a catalog of galaxy photometric redshifts (photo-z’s) for the Sloan Digital Sky Survey (SDSS) Data Release 6 (DR6). We use the neural network (NN) technique to calculate photo-z’s and the nearest neighbor error (NNE) method to estimate photo-z errors for ~77 million objects classified as galaxies in DR6 with r < 22. The photo-z and photo-z error estimators are trained and validated on a sample of ~640,000 galaxies that have SDSS photometry and spectroscopic redshifts measured by SDSS, the Two Degree Field, the SDSS Luminous Red Galaxy and Quasi-stellar Object Survey (2SLAQ), the Canada-France Redshift Survey (CFRS), the Canadian Network for Observational Cosmology Field Galaxy Survey (CNOC2), the Team Keck Redshift Survey (TKRS), the Deep Extragalactic Evolutionary Probe (DEEP), and DEEP2. For the two best NN methods we have tried, we find that 68% of the galaxies in the validation set have a photo-z error smaller than σ68 = 0.021 or 0.024. After presenting our results and quality tests, we provide a short guide for users accessing the public data.

Subfields: Astronomy and Astrophysics, Instrumentation

View Paper: https://www.semanticscholar.org/paper/22b134afd37df56fce8727016dac991e7ab06f57

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Citations in the corpus

Estimating the redshift distribution of photometric galaxy samples – II. Applications and tests of a new method (2008, methodology): “For comparison with the weighting method, we use an Artificial Neural Network (ANN) method to estimate photometric redshifts (Collister & Lahav 2004; Oyaizu et al. 2008) We use a particular type of ANN called a Feed Forward Multilayer Perceptron (FFMP), which consists of several nodes arranged in…”
THE SEVENTH DATA RELEASE OF THE SLOAN DIGITAL SKY SURVEY (2008, methodology): “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.”
Photometric redshift analysis in the Dark Energy Survey Science Verification data (2014, methodology): “We also computed accompanying photo-z errors using the empirical nearest neighbor error (NNE) technique, described in detail by Oyaizu et al. (2008a).”
FIRST-YEAR SLOAN DIGITAL SKY SURVEY-II SUPERNOVA RESULTS: HUBBLE DIAGRAM AND COSMOLOGICAL PARAMETERS (2009, methodology): “For SN redshifts zSN < 0.4, noise from the host galaxy is simulated by associating the SN with a host from the SDSS galaxy photometric redshift catalog (Oyaizu et al. 2008), randomly selected such that zgal ∼ zSN.”
CHARACTERIZING THE MID-INFRARED EXTRAGALACTIC SKY WITH WISE AND SDSS (2012, methodology): “In addition, all SDSS photometric redshifts (when spectroscopic redshifts are not avail- able) used in sections below are the ones derived based on the neural network method (Oyaizu et al. 2008).”
CONSTRAINING THE SCATTER IN THE MASS–RICHNESS RELATION OF maxBCG CLUSTERS WITH WEAK LENSING AND X-RAY DATA (2008, methodology): “These shear profiles were turned into surface mass density contrast profiles using the redshift distribution of background sources estimated with the methods of Lima et al. (2008) and the neural net photometric redshift estimators described in Oyaizu et al. (2008).”
HerMES: CANDIDATE GRAVITATIONALLY LENSED GALAXIES AND LENSING STATISTICS AT SUBMILLIMETER WAVELENGTHS (2012, background): “The the central group galaxy has a photometric redshift of 0.60± 0.04 (Oyaizu et al. 2008); photometric or spectroscopic redshifts are unavailable for the remaining group members due to their faintness.”
HYDROGEN-POOR SUPERLUMINOUS SUPERNOVAE AND LONG-DURATION GAMMA-RAY BURSTS HAVE SIMILAR HOST GALAXIES (2013, background): “13 of these have host galaxies that are well-detected in SDSS, and thus have photometric redshifts available (Oyaizu et al. 2008).”
THE COS-HALOS SURVEY: RATIONALE, DESIGN, AND A CENSUS OF CIRCUMGALACTIC NEUTRAL HYDROGEN (2013, background): “0.2 (Oyaizu et al. 2008), the photometric redshifts were adequate to ensure that the key ionization 12 Martin et al. (2005), http://galex.stsci.edu diagnostics of CGM gas, particularly H I and O VI, still fall within the 1150− 1800 Å range of the COS M gratings.”
GALAXY CLUSTERS IDENTIFIED FROM THE SDSS DR6 AND THEIR PROPERTIES (2009, methodology): “Oyaizu et al. (2008) estimated photometric redshifts with the Artificial Neural Network technique and provided two different photometric redshift estimates, CC2 and D1.”
DISSECTING PHOTOMETRIC REDSHIFT FOR ACTIVE GALACTIC NUCLEUS USING XMM- AND CHANDRA-COSMOS SAMPLES (2011, background): “While such a SDSS-like filter set can help to provide reliable photoz for normal galaxies up to z∼1 (Oyaizu et al. 2008), it is insufficient for AGN.”
THE BLANCO COSMOLOGY SURVEY: DATA ACQUISITION, PROCESSING, CALIBRATION, QUALITY DIAGNOSTICS, AND DATA RELEASE (2012, methodology): “Oyaizu et al. (2008a) evaluate how well various methods improve zphot statistics.”
MEASURING BARYON ACOUSTIC OSCILLATIONS ALONG THE LINE OF SIGHT WITH PHOTOMETRIC REDSHIFTS: THE PAU SURVEY (2008, unknown): “LRGs usually have higher photo-z precisions than the rest of the galaxies (as it happens with the COMBO-17 data or the SDSS LRGs; Oyaizu et al. 2008; D’Abrusco et al. 2007), and it is expected that LRGs in the ALHAMBRA Survey will have photo-z errors substantially below σz ∼ 0.01(1 + z).”
PHAT: PHoto-z Accuracy Testing (2010, methodology): “Neural networks have been used e.g. for estimation of photo-z’s for the SDSS (Collister et al. 2007; Oyaizu et al. 2008; Abdalla et al. 2008b), as well as forecasts of photometric redshifts for future surveys like the Dark Energy Survey (Banerji et al. 2008) and Euclid (Abdalla et al. 2008a).”