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Title: Dark Energy Survey Year 1 Results: Cross-Correlation Redshifts - Methods and Systematics Characterization

Abstract

We use numerical simulations to characterize the performance of a clustering-based method to calibrate photometric redshift biases. In particular, we cross-correlate the weak lensing (WL) source galaxies from the Dark Energy Survey Year 1 (DES Y1) sample with redMaGiC galaxies (luminous red galaxies with secure photometric red- shifts) to estimate the redshift distribution of the former sample. The recovered redshift distributions are used to calibrate the photometric redshift bias of standard photo-z methods applied to the same source galaxy sample. We also apply the method to three photo-z codes run in our simulated data: Bayesian Photometric Redshift (BPZ), Directional Neighborhood Fitting (DNF), and Random Forest-based photo-z (RF). We characterize the systematic uncertainties of our calibration procedure, and find that these systematic uncertainties dominate our error budget. The dominant systematics are due to our assumption of unevolving bias and clustering across each redshift bin, and to differences between the shapes of the redshift distributions derived by clustering vs photo-z's. The systematic uncertainty in the mean redshift bias of the source galaxy sample is z ≲ 0.02, though the precise value depends on the redshift bin under consideration. Here, we discuss possible ways to mitigate the impact of our dominant systematics inmore » future analyses.« less

Authors:
 [1]
  1. Barcelona Inst. of Science adn Technology (Spain). et al.
Publication Date:
Research Org.:
Brookhaven National Laboratory (BNL), Upton, NY (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); SLAC National Accelerator Lab., Menlo Park, CA (United States); Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
Contributing Org.:
DES Collaboration
OSTI Identifier:
1439283
Alternate Identifier(s):
OSTI ID: 1399673; OSTI ID: 1468040
Report Number(s):
BNL-114383-2017-JA; FERMILAB-PUB-17-317-A-AE; arXiv:1709.00992
Journal ID: ISSN 0035-8711
Grant/Contract Number:  
SC0012704; AC02-07CH11359; AC05-00OR22725
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Monthly Notices of the Royal Astronomical Society
Additional Journal Information:
Journal Volume: 477; Journal Issue: 2; Journal ID: ISSN 0035-8711
Publisher:
Royal Astronomical Society
Country of Publication:
United States
Language:
English
Subject:
79 ASTRONOMY AND ASTROPHYSICS; galaxies: distances and redshifts; cosmology: observations

Citation Formats

Gatti, M. Dark Energy Survey Year 1 Results: Cross-Correlation Redshifts - Methods and Systematics Characterization. United States: N. p., 2018. Web. doi:10.1093/mnras/sty466.
Gatti, M. Dark Energy Survey Year 1 Results: Cross-Correlation Redshifts - Methods and Systematics Characterization. United States. doi:10.1093/mnras/sty466.
Gatti, M. Thu . "Dark Energy Survey Year 1 Results: Cross-Correlation Redshifts - Methods and Systematics Characterization". United States. doi:10.1093/mnras/sty466.
@article{osti_1439283,
title = {Dark Energy Survey Year 1 Results: Cross-Correlation Redshifts - Methods and Systematics Characterization},
author = {Gatti, M.},
abstractNote = {We use numerical simulations to characterize the performance of a clustering-based method to calibrate photometric redshift biases. In particular, we cross-correlate the weak lensing (WL) source galaxies from the Dark Energy Survey Year 1 (DES Y1) sample with redMaGiC galaxies (luminous red galaxies with secure photometric red- shifts) to estimate the redshift distribution of the former sample. The recovered redshift distributions are used to calibrate the photometric redshift bias of standard photo-z methods applied to the same source galaxy sample. We also apply the method to three photo-z codes run in our simulated data: Bayesian Photometric Redshift (BPZ), Directional Neighborhood Fitting (DNF), and Random Forest-based photo-z (RF). We characterize the systematic uncertainties of our calibration procedure, and find that these systematic uncertainties dominate our error budget. The dominant systematics are due to our assumption of unevolving bias and clustering across each redshift bin, and to differences between the shapes of the redshift distributions derived by clustering vs photo-z's. The systematic uncertainty in the mean redshift bias of the source galaxy sample is z ≲ 0.02, though the precise value depends on the redshift bin under consideration. Here, we discuss possible ways to mitigate the impact of our dominant systematics in future analyses.},
doi = {10.1093/mnras/sty466},
journal = {Monthly Notices of the Royal Astronomical Society},
number = 2,
volume = 477,
place = {United States},
year = {Thu Feb 22 00:00:00 EST 2018},
month = {Thu Feb 22 00:00:00 EST 2018}
}

Journal Article:
Free Publicly Available Full Text
This content will become publicly available on February 22, 2019
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Cited by: 4 works
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