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 highdimensional 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 selfconsistently compare the new method to existing ones from the literature in the context of a forecasted cosmic shear analysis for the HSC threeyear shape catalogue, and find that these methods recover statistically consistent error bars for the cosmological parameter constraints for predicted HSC threeyear 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.
more » « less Award ID(s):
 2020295
 NSFPAR ID:
 10476083
 Publisher / Repository:
 Royal Astronomical Society
 Date Published:
 Journal Name:
 Monthly Notices of the Royal Astronomical Society
 Volume:
 518
 Issue:
 1
 ISSN:
 00358711
 Page Range / eLocation ID:
 709 to 723
 Format(s):
 Medium: X
 Sponsoring Org:
 National Science Foundation
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