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Title: Estimating redshift distributions using hierarchical logistic Gaussian processes

Abstract

ABSTRACT 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.

Authors:
ORCiD logo [1];  [2]; ORCiD logo [1]
  1. McWilliams Center for Cosmology, Department of Physics, Carnegie Mellon University, Pittsburgh, PA 15213, USA
  2. School of Computer Science and Statistics, Lloyd Institute, Trinity College, Dublin, Ireland
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1579995
Grant/Contract Number:  
DESC0011114
Resource Type:
Published Article
Journal Name:
Monthly Notices of the Royal Astronomical Society
Additional Journal Information:
Journal Name: Monthly Notices of the Royal Astronomical Society Journal Volume: 491 Journal Issue: 4; Journal ID: ISSN 0035-8711
Publisher:
Oxford University Press
Country of Publication:
United Kingdom
Language:
English

Citation Formats

Rau, Markus Michael, Wilson, Simon, and Mandelbaum, Rachel. Estimating redshift distributions using hierarchical logistic Gaussian processes. United Kingdom: N. p., 2019. Web. doi:10.1093/mnras/stz3295.
Rau, Markus Michael, Wilson, Simon, & Mandelbaum, Rachel. Estimating redshift distributions using hierarchical logistic Gaussian processes. United Kingdom. doi:10.1093/mnras/stz3295.
Rau, Markus Michael, Wilson, Simon, and Mandelbaum, Rachel. Tue . "Estimating redshift distributions using hierarchical logistic Gaussian processes". United Kingdom. doi:10.1093/mnras/stz3295.
@article{osti_1579995,
title = {Estimating redshift distributions using hierarchical logistic Gaussian processes},
author = {Rau, Markus Michael and Wilson, Simon and Mandelbaum, Rachel},
abstractNote = {ABSTRACT 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.},
doi = {10.1093/mnras/stz3295},
journal = {Monthly Notices of the Royal Astronomical Society},
number = 4,
volume = 491,
place = {United Kingdom},
year = {2019},
month = {11}
}

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