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redMaGiC: Selecting Luminous Red Galaxies from the DES Science Verification Data

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arxiv 1507.05460 v1 pith:2HHBPUOI submitted 2015-07-20 astro-ph.IM astro-ph.COastro-ph.GA

redMaGiC: Selecting Luminous Red Galaxies from the DES Science Verification Data

E. Rozo , E. S. Rykoff , A. Abate , C. Bonnett , M. Crocce , C. Davis , B. Hoyle , B. Leistedt
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H.V. Peiris R. H. Wechsler T. Abbott F. B. Abdalla M. Banerji A. H. Bauer A. Benoit-L\'evy G. M. Bernstein E. Bertin D. Brooks E. Buckley-Geer D. L. Burke D. Capozzi A. Carnero Rosell D. Carollo M. Carrasco Kind J. Carretero F. J. Castander M. J. Childress C. E. Cunha C. B. D'Andrea T. Davis D. L. DePoy S. Desai H. T. Diehl J. P. Dietrich P. Doel T. F. Eifler A. E. Evrard A. Fausti Neto B. Flaugher P. Fosalba J. Frieman E. Gaztanaga D. W. Gerdes K. Glazebrook D. Gruen R. A. Gruendl K. Honscheid D. J. James M. Jarvis A. G. Kim K. Kuehn N. Kuropatkin O. Lahav C. Lidman M. Lima M. A. G. Maia M. March P. Martini P. Melchior C. J. Miller R. Miquel J. J. Mohr R. C. Nichol B. Nord C. R. O'Neill R. Ogando A. A. Plazas A. K. Romer A. Roodman M. Sako E. Sanchez B. Santiago M. Schubnell I. Sevilla-Noarbe R. C. Smith M. Soares-Santos F. Sobreira E. Suchyta M. E. C. Swanson J. Thaler D. Thomas S. Uddin V. Vikram A. R. Walker W. Wester Y. Zhang L. N. da Costa
This is my paper
classification astro-ph.IM astro-ph.COastro-ph.GA
keywords redmagicalgorithmdatabiasescomovingdensitygalaxiesluminous
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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 color-cuts necessary to produce a luminosity-thresholded LRG sample of constant comoving density. We demonstrate that redMaGiC photozs 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 catalog sampling the redshift range $z\in[0.2,0.8]$. Our fiducial sample has a comoving space density of $10^{-3}\ (h^{-1} Mpc)^{-3}$, and a median photoz 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%. We also test our algorithm with Sloan Digital Sky Survey (SDSS) Data Release 8 (DR8) and Stripe 82 data, and discuss how spectroscopic training can be used to control photoz biases at the 0.1% level.

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