Gaussian process-based Bayesian imaging of bolometric (sub-)millimetre data
Widefield single-dish telescopes operating at (sub-)mm wavelengths ($λ\sim 4$~mm to 350~$μ$m) tend to feature much faster mapping speeds than radio interferometers at the same wavelengths, and are more suitable for measuring signals on larger spatial scales. However, the data are dominated by atmospheric fluctuations on large scales and by photon and instrumental noise on smaller scales, making it difficult to recover the true astronomical sky. We build on maria-nifty, a Gaussian process-based Bayesian imaging framework for reconstructing sky maps from single-dish telescope data, implemented on top of Numerical Information Field Theory (NIFTy). It uses modular generative models to efficiently separate the astronomical signal from atmospheric emission while providing pixel-wise uncertainty quantification for the reconstructed sky maps. Moreover, our method cleanly separates individual astrophysical signals, even though they are superposed on the sky. Following its successful validation on synthetic time-ordered data generated using the maria software, we apply it to real data observed with the MUSTANG-2 bolometric array on the 100-meter Green Bank Telescope. For the galaxy cluster RXJ1347.5-1145, we separate the extended cluster emission from the compact source at its centre and obtain a smoother map with fewer artefacts compared to a standard maximum-likelihood reconstruction using the same data. The JAX-based implementation and GPU acceleration provide the basis for applying the method to the vastly larger datasets that the next generation of single-dish telescopes will deliver.