  The treatment of unknown foreground contaminations will be one of the major
challenges for galaxy clustering analyses of coming decadal surveys. These data
contaminations introduce erroneous large-scale effects in recovered power
spectra and inferred dark matter density fields. In this work, we present an
effective solution to this problem in the form of a robust likelihood designed
to account for effects due to unknown foreground and target contaminations.
Conceptually, this robust likelihood marginalizes over the unknown large-scale
contamination amplitudes. We showcase the effectiveness of this novel
likelihood via an application to a mock SDSS-III data set subject to dust
extinction contamination. In order to illustrate the performance of our
proposed likelihood, we infer the underlying dark-matter density field and
reconstruct the matter power spectrum, being maximally agnostic about the
foregrounds. The results are compared to those of an analysis with a standard
Poissonian likelihood, as typically used in modern large-scale structure
analyses. While the standard Poissonian analysis yields excessive power for
large-scale modes and introduces an overall bias in the power spectrum, our
likelihood provides unbiased estimates of the matter power spectrum over the
entire range of Fourier modes considered in this work. Further, we demonstrate
that our approach accurately accounts for and corrects the effects of unknown
foreground contaminations when inferring three-dimensional density fields.
Robust likelihood approaches, as presented in this work, will be crucial to
control unknown systematic error and maximize the outcome of the decadal
surveys.
