10-1093-mnras-stw1281.pdf (3.33 MB)
redMaGiC: selecting luminous red galaxies from the DES Science Verification data
journal contribution
posted on 2023-06-09, 03:12 authored by Kathy RomerKathy Romer, et al. The DES CollaborationWe 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 ? [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 (zspec - zphoto) and scatter (sigmaz/(1 + z)) of 0.005 and 0.017, respectively. The corresponding 5sigma 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.
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STFC
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- Published
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- Published version
Journal
Monthly Notices Of The Royal Astronomical SocietyISSN
0035-8711Publisher
Oxford University PressExternal DOI
Issue
2Volume
461Page range
1431-1450Department affiliated with
- Physics and Astronomy Publications
Research groups affiliated with
- Astronomy Centre Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2016-09-30First Open Access (FOA) Date
2016-09-30First Compliant Deposit (FCD) Date
2016-09-30Usage metrics
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