Calibrating photometric redshifts of luminous red galaxies


Autoria(s): Padmanabhan, Nikhil; Budavari, Tamas; Schlegel, David J.; Bridges, Terry; Brinkmann, Jonathan; Cannon, Russell; Connolly, Andrew J.; Croom, Scott M.; Csabai, Istvan; Drinkwater, Michael.; Eisenstein, Daniel J.; Hewett, Paul C.; Loveday, Jon; Nichol, Robert C.; Pimbblet, Kevin A.; De Propris, R.; Schneider, D. P.; Scranton, R.; Seljak, U.; Shanks, T.; Szapudi, I.; Szalay, A. S.; Wake, D
Contribuinte(s)

A. C. Fabian

Data(s)

01/05/2005

Resumo

We discuss the construction of a photometric redshift catalogue of luminous red galaxies (LRGs) from the Sloan Digital Sky Survey (SDSS), emphasizing the principal steps necessary for constructing such a catalogue: (i) photometrically selecting the sample, (ii) measuring photometric redshifts and their error distributions, and (iii) estimating the true redshift distribution. We compare two photometric redshift algorithms for these data and find that they give comparable results. Calibrating against the SDSS and SDSS-2dF (Two Degree Field) spectroscopic surveys, we find that the photometric redshift accuracy is sigma similar to 0.03 for redshifts less than 0.55 and worsens at higher redshift (similar to 0.06 for z < 0.7). These errors are caused by photometric scatter, as well as systematic errors in the templates, filter curves and photometric zero-points. We also parametrize the photometric redshift error distribution with a sum of Gaussians and use this model to deconvolve the errors from the measured photometric redshift distribution to estimate the true redshift distribution. We pay special attention to the stability of this deconvolution, regularizing the method with a prior on the smoothness of the true redshift distribution. The methods that we develop are applicable to general photometric redshift surveys.

Identificador

http://espace.library.uq.edu.au/view/UQ:76101

Idioma(s)

eng

Publicador

Oxford University Press

Palavras-Chave #Astronomy & Astrophysics #Catalogues #Surveys #Galaxies : fundamental parameters #Digital Sky Survey #Hubble Deep Field #Spectroscopic Target Selection #Artificial Neural-networks #Broad-band Photometry #Early Data Release #Evolution #Distributions #Algorithm #Catalog
Tipo

Journal Article