Estimating redshift distributions using hierarchical logistic Gaussian processes

Markus Michael Rau

doi:10.1093/mnras/stz3295

2019-11-26

Markus Michael Rau *

Simon Wilson

Rachel Mandelbaum

DOI：10.1093/mnras/stz3295

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摘要

En 中文

This work uses hierarchical logistic Gaussian processes to infer true redshift distributions of samples of galaxies, through their cross-correlations with spatially overlapping spectroscopic samples. We demonstrate that this method can accurately estimate these redshift distributions in a fully Bayesian manner jointly with galaxy-dark matter bias models. We forecast how systematic biases in the redshift-dependent galaxy-dark matter bias model affect redshift inference. Using published galaxy-dark matter bias measurements from the Illustris simulation, we compare these systematic biases with the statistical error budget from a forecasted weak gravitational lensing measurement. If the redshift-dependent galaxy-dark matter bias model is mis-specified, redshift inference can be biased. This can propagate into relative biases in the weak lensing convergence power spectrum on the 10-30 per cent level. We, therefore, showcase a methodology to detect these sources of error using Bayesian model selection techniques. Furthermore, we discuss the improvements that can be gained from incorporating prior information from Bayesian template fitting into the model, both in redshift prediction accuracy and in the detection of systematic modelling biases.

Keyword:

catalogues

surveys

galaxies: distances and redshifts

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期刊

Monthly Notices of the Royal Astronomical Society

IF:

4.8

论文数:

7.1W

被引数:

25.0W

机构

Carnegie Mellon University

学者数:

1.4W

论文数: 1.4W

被引数: 2.7W

Trinity College Dublin

学者数:

2.4W

论文数: 1.9W

被引数: 2.7W

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