Gaussian process-based Bayesian imaging of bolometric (sub-)millimetre data
The study introduces maria-nifty, a Gaussian process-based Bayesian framework for reconstructing sky maps from single-dish sub-millimeter data. It uses modular generative models to separate astronomical signals from atmospheric noise and provides uncertainty quantification. Applied to the RXJ1347 galaxy cluster, the method cleanly separates extended cluster emission from a compact source, yielding a smoother map than traditional maximum-likelihood methods.