Title: Bayesian power-spectrum inference with foreground and target contamination treatment,
Abstract: This work presents a joint and self-consistent Bayesian treatment of various
foreground and target contaminations when inferring cosmological power-spectra
and three dimensional density fields from galaxy redshift surveys. This is
achieved by introducing additional block sampling procedures for unknown
coefficients of foreground and target contamination templates to the previously
presented ARES framework for Bayesian large scale structure analyses. As a
result the method infers jointly and fully self-consistently three dimensional
density fields, cosmological power-spectra, luminosity dependent galaxy biases,
noise levels of respective galaxy distributions and coefficients for a set of a
priori specified foreground templates. In addition this fully Bayesian approach
permits detailed quantification of correlated uncertainties amongst all
inferred quantities and correctly marginalizes over observational systematic
effects. We demonstrate the validity and efficiency of our approach in
obtaining unbiased estimates of power-spectra via applications to realistic
mock galaxy observations subject to stellar contamination and dust extinction.
While simultaneously accounting for galaxy biases and unknown noise levels our
method reliably and robustly infers three dimensional density fields and
corresponding cosmological power-spectra from deep galaxy surveys. Further our
approach correctly accounts for joint and correlated uncertainties between
unknown coefficients of foreground templates and the amplitudes of the
power-spectrum. An effect amounting up to $10$ percent correlations and
anti-correlations across large ranges in Fourier space.