The Zurich Extragalactic Bayesian Redshift Analyzer and its first application: COSMOS

By Robert Feldmann, C. Carollo, C. Porciani, S. Lilly, P. Capak, Yoshiaki Taniguchi, O. Fèvre, A. Renzini, N. Scoville, M. Ajiki and 14 more. 2006

230 citations recorded by the source corpus.

We present the Zurich Extragalactic Bayesian Redshift Analyzer (ZEBRA). The current version of ZEBRA combines and extends several of the classical approaches to produce accurate photometric redshifts down to faint magnitudes. In particular, ZEBRA uses the template-fitting approach to produce Maximum Likelihood and Bayesian redshift estimates based on the following points. (i) An automatic iterative technique to correct the original set of galaxy templates to best represent the Spectral Energy Distributions (SEDs) of real galaxies at different redshifts. (ii) A training set of spectroscopic redshifts for a small fraction of the photometric sample to improve the robustness of the photometric redshift estimates. (iii) An iterative technique for Bayesian redshift estimates, which extracts the full two-dimensional redshift and template probability function for each galaxy. We demonstrate the performance of ZEBRA by applying it to a sample of 866 I_AB ⩽ 22.5 COSMOS galaxies with available u*, B, V, g', r', i', z' and K_s photometry and zCOSMOS spectroscopic redshifts in the range 0 < z < 1.3 . Adopting a 5σ clipping that excludes ⩽10 galaxies, both the Maximum Likelihood and Bayesian ZEBRA estimates for this sample have an accuracy σ_(Δz/(1+z)) smaller than 0.03. Similar accuracies are recovered using mock galaxies.

Subfields: Astronomy and Astrophysics, Ecology, Instrumentation

View Paper: https://www.semanticscholar.org/paper/183daf10ccd93ed7fbf66f681a3e007dd465b165

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Citations in the corpus

THE MOST LUMINOUS z ∼ 9–10 GALAXY CANDIDATES YET FOUND: THE LUMINOSITY FUNCTION, COSMIC STAR-FORMATION RATE, AND THE FIRST MASS DENSITY ESTIMATE AT 500 Myr (2013, methodology): “1 distribution of the code.12 The best-fit EAZY redshifts are all within 0.1 of the ZEBRA values listed in Table 2.”
Analysis of galaxy spectral energy distributions from far-UV to far-IR with CIGALE: studying a SINGS test sample (2009, background): “Sophisticated fitting codes of galaxy SEDs mainly focus on the derivation of redshifts and relatively robust galaxy properties like masses (e.g., Bolzonella et al. 2000; Feldmann et al. 2006; Walcher et al. 2008).”
zCOSMOS: A Large VLT/VIMOS Redshift Survey Covering 0 < z < 3 in the COSMOS Field (2006, background): “The very impressive deep multiband photometry in the COSMOS field (Taniguchi et al. 2007; Capak et al. 2007) enables the approximate redshifts of vast numbers of galaxies to be estimated from their broadband spectral energydistributions (Mobasher et al. 2007; Feldmann et al. 2006).”
Weak Lensing Measurement of the Mass--Richness Relation of SDSS redMaPPer Clusters (2016, methodology): “ZEBRA is a template-fitting software; for this catalogue, four observed SEDs and two synthetic blue galaxy spectra from Beńıtez (2000) were used, along with twenty-five additional interpolated templates created between pairs of the six original templates.”
zCOSMOS – 10k-bright spectroscopic sample - The bimodality in the galaxy stellar mass function: exploring its evolution with redshift (2009, methodology): “…place (from 5 to 1, see Table 3 in Lilly et al. 2009) in the class is added to indicate the level of consistency between the spectroscopic and photometric redshifts obtained by the Zurich Extragalactic Bayesian Redshift Analyzer (ZEBRA; Feldmann et al. 2006), using the optical to infrared SED.”
CARS: the CFHTLS-Archive-Research Survey. I. Five-band multi-colour data from 37 sq. deg. CFHTLS-wid (2008, background): “The high-z underestimation of our redshifts can be corrected by a recalibration of the original Coleman et al. (1980) and Kinney et al. (1996) template sets; see also Feldmann et al. (2006).”
Mapping stellar content to dark matter haloes using galaxy clustering and galaxy–galaxy lensing in the SDSS DR7 (2015, methodology): “…a catalogue of background galaxies (Reyes et al. 2012) with a number density of 1.2 arcmin−2 with weak lensing shears estimated using the re-Gaussianization method (Hirata & Seljak 2003) and photometric redshifts from Zurich Extragalactic Bayesian Redshift Analyzer (ZEBRA, Feldmann et al. 2006).”
EAZY: A Fast, Public Photometric Redshift Code (2008, methodology): “However, given a set of objects with good quality photometry, the aspect that is of paramount importance for obtaining reliable photometric redshifts is the selection of the template set (see Feldmann et al. 2006, §2.2).”
COLOR–MAGNITUDE RELATIONS OF ACTIVE AND NON-ACTIVE GALAXIES IN THE CHANDRA DEEP FIELDS: HIGH-REDSHIFT CONSTRAINTS AND STELLAR-MASS SELECTION EFFECTS (2010, methodology): “…spectroscopic redshiftif available (see § 4.6.2 for more details); otherwise, we adopted photometric redshifts (Rafferty et al. 2010) derived with the Zurich Extragalactic Bayesian Redshift Analyzer (ZEBRA; Feldmann et al. 2006), using the ultradeep multiwavelength data described in § 2.1.”
A REMARKABLY LUMINOUS GALAXY AT Z = 11.1 MEASURED WITH HUBBLE SPACE TELESCOPE GRISM SPECTROSCOPY (2016, methodology): “This was done using the code ZEBRA+ (Oesch et al. 2010), an extension of the photometric redshift code ZEBRA (Feldmann et al. 2006).”
COSMOS Morphological Classification with the Zurich Estimator of Structural Types (ZEST) and the Evolution Since z = 1 of the Luminosity Function of Early, Disk, and Irregular Galaxies (2006, methodology): “The overdensity in the COSMOS field at 0.2 < z < 0.4 is confirmed by the spectroscopic (Lilly et al. 2006) and photometric (Feldmann et al. 2006) redshift distributions.”
Intrinsic alignments of SDSS-III BOSS LOWZ sample galaxies (2014, methodology): “For this purpose, we use the maximum-likelihood estimates of photometric redshifts (photo-z) based on the five-band photometry from the Zurich Extragalactic Bayesian Redshift Analyzer (ZEBRA, Feldmann et al. 2006), which were characterised by Nakajima et al. (2012) and Reyes et al. (2012).”
NEWLY QUENCHED GALAXIES AS THE CAUSE FOR THE APPARENT EVOLUTION IN AVERAGE SIZE OF THE POPULATION (2013, methodology): “…the compiled IRAC fluxes were measured in fixed apertures of 1.9 arcsec and are corrected to total by dividing out factors of 0.76, 0.74, 0.62, and 0.58 at 3.6µm, 4.5µm, 5.6µm, and 8.0µm, respectively; cf. Ilbert et al. 2009. package ZEBRA10 run in catalog-correction mode (Feldmann et al. 2006).”
The many flavours of photometric redshifts (2018, background): “EAZY [73] combines basic templates and creates new ones on-thefly, while ZEBRA [74] trains the templates using a spectroscopic sample (a risk, when the spectroscopic sample is not representative of the entire population [see Fig.”
PROBING THE DAWN OF GALAXIES AT z ∼ 9–12: NEW CONSTRAINTS FROM HUDF12/XDF AND CANDELS DATA (2013, methodology): “While Ellis et al. (2013) find a bestfit photometric redshift of 8.6 ± 0.7, the source is not present in the ‘robust’ sample of McLure et al. (2012), and we find a photometric redshift distribution function which is very broad, with a best-fit at z ∼ 0.5 (using both ZEBRA or EAZY).”
Photometric Redshifts of Galaxies in COSMOS (2006, methodology): “ZEBRA estimates redshifts and template types of galaxies using medium- and broadband photometric data (Feldmann et al. 2006).”
The LABOCA survey of the Extended Chandra Deep Field South: A photometric redshift survey of submillimetre galaxies (2010, methodology): “To assess the level of systematic uncertainties in the derived photometric redshifts due to the adopted methodology, SED templates, and/or photometric data, we also use ZEBRA (Zurich Extragalactic Bayesian Redshift Analyzer; Feldmann et al. 2006) to independently calculate photometric redshifts.”