Explicit Bayesian treatment of unknown foreground contaminations in galaxy surveys

Understand

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.