2005Monthly Notices of the Royal Astronomical SocietyOpen access

Calibrating photometric redshifts of luminous red galaxies

Nikhil Padmanabhan, Tamás Budavári, David J. Schlegel, Terry Bridges, J. Brinkmann, R. Cannon, Andrew J. Connolly, S. M. Croom, István Csabai, M. J. Drinkwater, Daniel J. Eisenstein, P. C. Hewett, J. Loveday, R. C. Nichol, Kevin A. Pimbblet, R. De Propris, D. P. Schneider, Ryan Scranton, Uroš Seljak, T. Shanks, István Szapudi, Alexander S. Szalay, David A. Wake

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Abstract

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 σ∼ 0.03 for redshifts less than 0.55 and worsens at higher redshift (∼ 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.

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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 σ∼ 0.03 for redshifts less than 0.55 and worsens at higher redshift (∼ 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.

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Available abstract

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 σ∼ 0.03 for redshifts less than 0.55 and worsens at higher redshift (∼ 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.

Key concepts: Photometric redshift, Redshift, Physics, Astrophysics, Galaxy, Photometry (optics), Sky, Cosmology

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