Estimating redshift distributions using Hierarchical Logistic Gaussian
  processes
  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% 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 modeling biases.
