Logo Logo
Switch Language to German

Rozo, E.; Rykoff, E. S.; Abate, A.; Bonnett, C.; Crocce, M.; Davis, C.; Hoyle, B.; Leistedt, B.; Peiris, H. V.; Wechsler, R. H.; Abbott, T.; Abdalla, F. B.; Banerji, M.; Bauer, A. H.; Benoit-Lévy, A.; Bernstein, G. M.; Bertin, E.; Brooks, D.; Buckley-Geer, E.; Burke, D. L.; Capozzi, D.; Rosell, A. Carnero; Carollo, D.; Kind, M. Carrasco; Carretero, J.; Castander, F. J.; Childress, M. J.; Cunha, C. E.; D'Andrea, C. B.; Davis, T.; Depoy, D. L.; Desai, S.; Diehl, H. T.; Dietrich, J. P.; Doel, P.; Eifler, T. F.; Evrard, A. E.; Fausti Neto, A.; Flaugher, B.; Fosalba, P.; Frieman, J.; Gaztanaga, E.; Gerdes, D. W.; Glazebrook, K.; Gruen, D.; Gruendl, R. A.; Honscheid, K.; James, D. J.; Jarvis, M.; Kim, A. G.; Kuehn, K.; Kuropatkin, N.; Lahav, O.; Lidman, C.; Lima, M.; Maia, M. A. G.; March, M.; Martini, P.; Melchior, P.; Miller, C. J.; Miquel, R.; Mohr, J. J.; Nichol, R. C.; Nord, B.; O'Neill, C. R.; Ogando, R.; Plazas, A. A.; Romer, A. K.; Roodman, A.; Sako, M.; Sanchez, E.; Santiago, B.; Schubnell, M.; Sevilla-Noarbe, I.; Smith, R. C.; Soares-Santos, M.; Sobreira, F.; Suchyta, E.; Swanson, M. E. C.; Thaler, J.; Thomas, D.; Uddin, S.; Vikram, V.; Walker, A. R.; Wester, W.; Zhang, Y. and Costa, L. N. da (2016): redMaGiC: selecting luminous red galaxies from the DES Science Verification data. In: Monthly Notices of the Royal Astronomical Society, Vol. 461, No. 2: pp. 1431-1450

Full text not available from 'Open Access LMU'.


We introduce redMaGiC, an automated algorithm for selecting luminous red galaxies (LRGs). The algorithm was specifically developed to minimize photometric redshift uncertainties in photometric large-scale structure studies. redMaGiC achieves this by self-training the colour cuts necessary to produce a luminosity-thresholded LRG sample of constant comoving density. We demonstrate that redMaGiC photo-zs are very nearly as accurate as the best machine learning-based methods, yet they require minimal spectroscopic training, do not suffer from extrapolation biases, and are very nearly Gaussian. We apply our algorithm to Dark Energy Survey (DES) Science Verification (SV) data to produce a redMaGiC catalogue sampling the redshift range z is an element of [0.2, 0.8]. Our fiducial sample has a comoving space density of 10(-3) (h(-1) Mpc)(-3), and a median photo-z bias (z(spec) - z(photo)) and scatter (sigma(z)/(1 + z)) of 0.005 and 0.017, respectively. The corresponding 5 sigma outlier fraction is 1.4 per cent. We also test our algorithm with Sloan Digital Sky Survey Data Release 8 and Stripe 82 data, and discuss how spectroscopic training can be used to control photo-z biases at the 0.1 per cent level.

Actions (login required)

View Item View Item