Volume 606, October 2017
|Number of page(s)||14|
|Section||Numerical methods and codes|
|Published online||05 October 2017|
Bayesian power spectrum inference with foreground and target contamination treatment
1 Excellence Cluster Universe, Technische Universität München, Boltzmannstrasse 2, 85748 Garching, Germany
2 CNRS & Sorbonne Universités, UPMC Univ. Paris 06, UMR 7095, Institut d’Astrophysique de Paris, 75014 Paris, France
3 Sorbonne Universités, Institut Lagrange de Paris (ILP), 98bis bd Arago, 75014 Paris, France
Received: 31 March 2017
Accepted: 24 May 2017
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 the 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 that are 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. Furthermore, our approach correctly accounts for joint and correlated uncertainties between unknown coefficients of foreground templates and the amplitudes of the power spectrum. This effect amounts to correlations and anti-correlations of up to 10 per cent across wide ranges in Fourier space.
Key words: large-scale structure of Universe / methods: statistical / methods: data analysis
© ESO, 2017
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