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Title: Selection biases in empirical p(z) methods for weak lensing

To measure the mass of foreground objects with weak gravitational lensing, one needs to estimate the redshift distribution of lensed background sources. This is commonly done in an empirical fashion, i.e. with a reference sample of galaxies of known spectroscopic redshift, matched to the source population. In this paper, we develop a simple decision tree framework that, under the ideal conditions of a large, purely magnitude-limited reference sample, allows an unbiased recovery of the source redshift probability density function p(z), as a function of magnitude and colour. We use this framework to quantify biases in empirically estimated p(z) caused by selection effects present in realistic reference and weak lensing source catalogues, namely (1) complex selection of reference objects by the targeting strategy and success rate of existing spectroscopic surveys and (2) selection of background sources by the success of object detection and shape measurement at low signal to noise. For intermediate-to-high redshift clusters, and for depths and filter combinations appropriate for ongoing lensing surveys, we find that (1) spectroscopic selection can cause biases above the 10 per cent level, which can be reduced to ≈5 per cent by optimal lensing weighting, while (2) selection effects in the shape catalogue biasmore » mass estimates at or below the 2 per cent level. Finally, this illustrates the importance of completeness of the reference catalogues for empirical redshift estimation.« less
Authors:
 [1] ;  [2]
  1. SLAC National Accelerator Lab., Menlo Park, CA (United States); Stanford Univ., CA (United States). Physics Dept. Kavli Inst. for Particle Astrophysics and Cosmology (KIPAC)
  2. Max Planck Inst. for Extraterrestrial Physics, Garching (Germany)
Publication Date:
Grant/Contract Number:
AC02-76SF00515; PF5-160138
Type:
Accepted Manuscript
Journal Name:
Monthly Notices of the Royal Astronomical Society
Additional Journal Information:
Journal Volume: 468; Journal Issue: 1; Journal ID: ISSN 0035-8711
Publisher:
Royal Astronomical Society
Research Org:
SLAC National Accelerator Lab., Menlo Park, CA (United States); Stanford Univ., CA (United States)
Sponsoring Org:
USDOE; National Aeronautic and Space Administration (NASA)
Contributing Orgs:
Max Planck Inst. for Extraterrestrial Physics, Garching (Germany)
Country of Publication:
United States
Language:
English
Subject:
79 ASTRONOMY AND ASTROPHYSICS; weak gravitational lensing; galaxy distances and redshifts; cosmology observations
OSTI Identifier:
1361132

Gruen, D., and Brimioulle, F.. Selection biases in empirical p(z) methods for weak lensing. United States: N. p., Web. doi:10.1093/mnras/stx471.
Gruen, D., & Brimioulle, F.. Selection biases in empirical p(z) methods for weak lensing. United States. doi:10.1093/mnras/stx471.
Gruen, D., and Brimioulle, F.. 2017. "Selection biases in empirical p(z) methods for weak lensing". United States. doi:10.1093/mnras/stx471. https://www.osti.gov/servlets/purl/1361132.
@article{osti_1361132,
title = {Selection biases in empirical p(z) methods for weak lensing},
author = {Gruen, D. and Brimioulle, F.},
abstractNote = {To measure the mass of foreground objects with weak gravitational lensing, one needs to estimate the redshift distribution of lensed background sources. This is commonly done in an empirical fashion, i.e. with a reference sample of galaxies of known spectroscopic redshift, matched to the source population. In this paper, we develop a simple decision tree framework that, under the ideal conditions of a large, purely magnitude-limited reference sample, allows an unbiased recovery of the source redshift probability density function p(z), as a function of magnitude and colour. We use this framework to quantify biases in empirically estimated p(z) caused by selection effects present in realistic reference and weak lensing source catalogues, namely (1) complex selection of reference objects by the targeting strategy and success rate of existing spectroscopic surveys and (2) selection of background sources by the success of object detection and shape measurement at low signal to noise. For intermediate-to-high redshift clusters, and for depths and filter combinations appropriate for ongoing lensing surveys, we find that (1) spectroscopic selection can cause biases above the 10 per cent level, which can be reduced to ≈5 per cent by optimal lensing weighting, while (2) selection effects in the shape catalogue bias mass estimates at or below the 2 per cent level. Finally, this illustrates the importance of completeness of the reference catalogues for empirical redshift estimation.},
doi = {10.1093/mnras/stx471},
journal = {Monthly Notices of the Royal Astronomical Society},
number = 1,
volume = 468,
place = {United States},
year = {2017},
month = {2}
}