# Bayesian power-spectrum inference with foreground and target contamination treatment

## TL;DR

This paper introduces a Bayesian framework for jointly inferring cosmological power-spectra and 3D density fields from galaxy surveys, effectively handling foreground contamination and systematic uncertainties.

## Contribution

It extends the ARES framework by incorporating block sampling for foreground and target contamination coefficients, enabling fully self-consistent and unbiased power-spectrum inference.

## Key findings

- Successfully infers unbiased power-spectra from mock data with stellar contamination.
- Accurately accounts for galaxy biases and noise levels in density field reconstruction.
- Quantifies correlated uncertainties, revealing up to 10% correlations across Fourier space.

## 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.