RedMaGiC: selecting luminous red galaxies from the DES Science Verification data. Issue 2 (30th May 2016)
- Record Type:
- Journal Article
- Title:
- RedMaGiC: selecting luminous red galaxies from the DES Science Verification data. Issue 2 (30th May 2016)
- Main Title:
- RedMaGiC: selecting luminous red galaxies from the DES Science Verification data
- Authors:
- 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.
Neto, A. Fausti
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.
da Costa, L. N.
… (more) - Abstract:
- Abstract: 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- z s 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 ∈ [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 (σ z /(1 + z )) of 0.005 and 0.017, respectively. The corresponding 5σ 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.
- Is Part Of:
- Monthly notices of the Royal Astronomical Society. Volume 461:Issue 2(2016)
- Journal:
- Monthly notices of the Royal Astronomical Society
- Issue:
- Volume 461:Issue 2(2016)
- Issue Display:
- Volume 461, Issue 2 (2016)
- Year:
- 2016
- Volume:
- 461
- Issue:
- 2
- Issue Sort Value:
- 2016-0461-0002-0000
- Page Start:
- 1431
- Page End:
- 1450
- Publication Date:
- 2016-05-30
- Subjects:
- methods: statistical -- techniques: photometric -- galaxies: general
Astronomy -- Periodicals
Periodicals
520.5 - Journal URLs:
- http://mnras.oxfordjournals.org/ ↗
http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1365-2966 ↗
http://www.blackwell-synergy.com/issuelist.asp?journal=mnr ↗
http://www.blackwell-synergy.com/loi/mnr ↗
http://ukcatalogue.oup.com/ ↗ - DOI:
- 10.1093/mnras/stw1281 ↗
- Languages:
- English
- ISSNs:
- 0035-8711
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 5943.000000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 20839.xml