Joint Bayesian calibration and map-making for intensity mapping experiments

Abstract

Line-intensity mapping (LIM) is an emerging cosmological technique that traces large-scale structure through the integrated spectral-line emission of unresolved sources. Reconstructing unbiased sky maps requires careful joint treatment of instrumental calibration and map-making, a task made challenging by time-varying receiver gains, thermal drifts, and correlated $1/f$ noise intrinsic to single-dish radio telescopes. We present a Bayesian framework for joint calibration and map-making using Gibbs sampling, giving access to the full joint posterior of calibration and sky map parameters. Our data model is grounded in the radiometer equation, capturing the coupling between noise level and system temperature without assuming a fixed noise amplitude. Gain and system temperature are estimated via an iterative generalized least-squares scheme, while absolute flux calibration is achieved either with external calibrators or via known signal injections such as noise diodes. We further introduce a $1/f$ noise model that avoids spurious periodic correlations arising from the common assumption of a diagonally structured noise covariance in the frequency domain. The workflow is implemented in an efficient software package using the Levinson algorithm and a polynomial emulator to reduce computational cost. Demonstrated on simulations representative of MeerKLASS single-dish observations, the framework generalizes to other single-dish surveys and to cross-correlation and interferometric data.