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Title: redMaGiC: selecting luminous red galaxies from the DES Science Verification data

Abstract

We introduce redMaGiC, an automated algorithm for selecting Luminous Red Galaxies (LRGs). The algorithm was 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 sam- ple of constant comoving density. Additionally, 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 catalog sampling the redshift range z ϵ [0.2,0.8]. Our fiducial sample has a comoving space density of 10 -3 (h -1Mpc) -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%. 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 photo-z biases at the 0.1% level.

Authors:
 [1]
  1. Univ. of Arizona, Tucson, AZ (United States). et al.
Publication Date:
Research Org.:
Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
Contributing Org.:
DES Collaboration
OSTI Identifier:
1223232
Report Number(s):
FERMILAB-PUB-15-337-AE-PPD
Journal ID: ISSN 0035-8711; arXiv eprint number arXiv:1507.05460
DOE Contract Number:  
AC02-07CH11359
Resource Type:
Journal Article
Resource Relation:
Journal Name: Monthly Notices of the Royal Astronomical Society; Journal Volume: 461; Journal Issue: 2
Country of Publication:
United States
Language:
English
Subject:
79 ASTRONOMY AND ASTROPHYSICS

Citation Formats

Rozo, E. redMaGiC: selecting luminous red galaxies from the DES Science Verification data. United States: N. p., 2016. Web. doi:10.1093/mnras/stw1281.
Rozo, E. redMaGiC: selecting luminous red galaxies from the DES Science Verification data. United States. doi:10.1093/mnras/stw1281.
Rozo, E. Mon . "redMaGiC: selecting luminous red galaxies from the DES Science Verification data". United States. doi:10.1093/mnras/stw1281. https://www.osti.gov/servlets/purl/1223232.
@article{osti_1223232,
title = {redMaGiC: selecting luminous red galaxies from the DES Science Verification data},
author = {Rozo, E.},
abstractNote = {We introduce redMaGiC, an automated algorithm for selecting Luminous Red Galaxies (LRGs). The algorithm was 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 sam- ple of constant comoving density. Additionally, 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 catalog sampling the redshift range z ϵ [0.2,0.8]. Our fiducial sample has a comoving space density of 10-3 (h-1Mpc)-3, and a median photo-z bias (zspec zphoto) and scatter (σz=(1 + z)) of 0.005 and 0.017 respectively.The corresponding 5σ 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 photo-z biases at the 0.1% level.},
doi = {10.1093/mnras/stw1281},
journal = {Monthly Notices of the Royal Astronomical Society},
number = 2,
volume = 461,
place = {United States},
year = {Mon May 30 00:00:00 EDT 2016},
month = {Mon May 30 00:00:00 EDT 2016}
}