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Title: Galaxy bias from the Dark Energy Survey Science Verification data: combining galaxy density maps and weak lensing maps

Journal Article · · Monthly Notices of the Royal Astronomical Society
DOI:https://doi.org/10.1093/mnras/stw861· OSTI ID:1244414

We measure the redshift evolution of galaxy bias from a magnitude-limited galaxy sample by combining the galaxy density maps and weak lensing shear maps for a $$\sim$$116 deg$$^{2}$$ area of the Dark Energy Survey (DES) Science Verification data. This method was first developed in Amara et al. (2012) and later re-examined in a companion paper (Pujol et al., in prep) with rigorous simulation tests and analytical treatment of tomographic measurements. In this work we apply this method to the DES SV data and measure the galaxy bias for a magnitude-limited galaxy sample. We find the galaxy bias and 1$$\sigma$$ error bars in 4 photometric redshift bins to be 1.33$$\pm$$0.18 (z=0.2-0.4), 1.19$$\pm$$0.23 (z=0.4-0.6), 0.99$$\pm$$0.36 ( z=0.6-0.8), and 1.66$$\pm$$0.56 (z=0.8-1.0). These measurements are consistent at the 1-2$$\sigma$$ level with mea- surements on the same dataset using galaxy clustering and cross-correlation of galaxies with CMB lensing. In addition, our method provides the only $$\sigma_8$$-independent constraint among the three. We forward-model the main observational effects using mock galaxy catalogs by including shape noise, photo-z errors and masking effects. We show that our bias measurement from the data is consistent with that expected from simulations. With the forthcoming full DES data set, we expect this method to provide additional constraints on the galaxy bias measurement from more traditional methods. Furthermore, in the process of our measurement, we build up a 3D mass map that allows further exploration of the dark matter distribution and its relation to galaxy evolution.

Research Organization:
Argonne National Laboratory (ANL), Argonne, IL (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Brookhaven National Laboratory (BNL), Upton, NY (United States); SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States); Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
Sponsoring Organization:
USDOE Office of Science (SC), High Energy Physics (HEP)
Contributing Organization:
DES
DOE Contract Number:
AC02-07CH11359
OSTI ID:
1244414
Report Number(s):
arXiv:1601.00405; FERMILAB-PUB-16-010-AE; 1412021
Journal Information:
Monthly Notices of the Royal Astronomical Society, Vol. 459, Issue 3; ISSN 0035-8711
Publisher:
Royal Astronomical Society
Country of Publication:
United States
Language:
English

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Cited By (14)

Lensing corrections to the E g ( z ) statistics from large scale structure journal September 2016
The Dark Energy Survey: more than dark energy – an overview journal March 2016
Improving weak lensing mass map reconstructions using Gaussian and sparsity priors: application to DES SV journal May 2018
Measuring linear and non-linear galaxy bias using counts-in-cells in the Dark Energy Survey Science Verification data journal October 2018
Weak lensing magnification in the Dark Energy Survey Science Verification data journal February 2018
Dark Energy Survey year 1 results: Galaxy-galaxy lensing journal August 2018
Neutrino Mass Ordering from Oscillations and Beyond: 2018 Status and Future Prospects journal October 2018
Improving weak lensing mass map reconstructions using Gaussian and sparsity priors: Application to DES SV text January 2018
The Dark Energy Survey: more than dark energy - an overview text January 2016
Lensing corrections to the $E_g(z)$ statistics from large scale structure text January 2016
Determining the progenitors of merging black-hole binaries text January 2016
Dark Energy Survey Year 1 Results: Cosmological Constraints from Galaxy Clustering and Weak Lensing text January 2017
Dark Energy Survey Year 1 Results: Galaxy-Galaxy Lensing text January 2017
Measuring Linear and Non-linear Galaxy Bias Using Counts-in-Cells in the Dark Energy Survey Science Verification Data text January 2018

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