maria goes NIFTy: Gaussian Process-Based Reconstruction and Denoising of Simulated (Sub-)Millimetre Single-Dish Telescope Data

Abstract

(Sub-)millimetre single-dish telescopes feature faster mapping speeds and access larger spatial scales than their interferometric counterparts. However, atmospheric fluctuations tend to dominate their signals and complicate recovery of the astronomical sky. Here we develop a framework for Gaussian process-based sky reconstruction and separation of the atmospheric emission from the astronomical signal based on Numerical Information Field Theory (NIFTy). To validate this novel approach, we use the maria software to generate synthetic time-ordered observational data mimicking the MUSTANG-2 bolometric array. This approach leads to significantly improved sky reconstructions versus traditional methods.

Figures (2)

• Figure 1: Output samples from our Bayesian reconstruction method, showing the variation in the NIFTy sky reconstructions through VI. Each panel is 5$^\prime$ across. For comparison, the ground truth map can be seen in the upper right panel of Fig. \ref{['fig:final_map_comp']}, while the mean NIFTy reconstruction and residuals are in the lower left and central panels of the figure.
• Figure 2: Compilation of the final maps and power spectral densities. The upper left shows the maximum likelihood map produced by mapping synthetic data from maria using minkasiminkasi. The upper middle shows the residuals on this reconstruction after subtracting the beam-smoothed input map (upper right panel). Each map panel is 5$^\prime$ across. The lower left and lower central panels show, respectively, the NIFTy reconstruction and residuals. The final, lower right panel shows the power spectral density of the data and fit components. In particular, the true and NIFTy-predicted map TODs closely follow each other from $\approx$2 Hz, where the array noise dominates, down to $\lesssim 1$ mHz, which for a typical scan speed of $50"$ s$^{-1}$ corresponds to scales $\gtrsim 13.9^{\circ}$.