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Title: Photometric redshift uncertainties in weak gravitational lensing shear analysis: models and marginalization
ABSTRACT

Recovering credible cosmological parameter constraints in a weak lensing shear analysis requires an accurate model that can be used to marginalize over nuisance parameters describing potential sources of systematic uncertainty, such as the uncertainties on the sample redshift distribution n(z). Due to the challenge of running Markov chain Monte Carlo (MCMC) in the high-dimensional parameter spaces in which the n(z) uncertainties may be parametrized, it is common practice to simplify the n(z) parametrization or combine MCMC chains that each have a fixed n(z) resampled from the n(z) uncertainties. In this work, we propose a statistically principled Bayesian resampling approach for marginalizing over the n(z) uncertainty using multiple MCMC chains. We self-consistently compare the new method to existing ones from the literature in the context of a forecasted cosmic shear analysis for the HSC three-year shape catalogue, and find that these methods recover statistically consistent error bars for the cosmological parameter constraints for predicted HSC three-year analysis, implying that using the most computationally efficient of the approaches is appropriate. However, we find that for data sets with the constraining power of the full HSC survey data set (and, by implication, those upcoming surveys with even tighter constraints), the choice of method for marginalizing over n(z) uncertainty among the several methods from the literature may modify the 1σ uncertainties on Ωm–S8 constraints by ∼4 per cent, and a careful model selection is needed to ensure credible parameter intervals.

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Award ID(s):
2020295
NSF-PAR ID:
10476083
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
Royal Astronomical Society
Date Published:
Journal Name:
Monthly Notices of the Royal Astronomical Society
Volume:
518
Issue:
1
ISSN:
0035-8711
Page Range / eLocation ID:
709 to 723
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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