Speaker
Description
(Sub-)millimeter single-dish telescopes observe larger spatial scales and feature faster mapping speeds than radio interferometers. However, their measured signals are dominated by atmospheric fluctuations and instrumental noise, making it difficult to recover the true astronomical sky. We introduce maria-nifty, a Gaussian process-based framework for reconstructing sky maps from single-dish telescope data, implemented on top of Numerical Information Field Theory (NIFTy). It uses modular generative models consisting of several components to efficiently separate the astronomical signal from atmospheric emissions while providing uncertainty quantification for the results. Following its successful validation on synthetic time-ordered data generated using the maria software [1], we now apply it to real data observed with the MUSTANG-2 bolometric array on the 100-meter Green Bank Telescope. This results in improved sky reconstructions compared to traditional methods, yielding higher-resolution sky maps with fewer artifacts.
[1] Würzinger, J. et al. 2025, arXiv e-prints [https://arxiv.org/abs/2509.01600]