Speaker
Description
The advent of the new generation of instrumentation in astrophysics like eROSITA poses several challenges due to the high-dimensional signals that vary in space, time, and energy. These typically have non-trivial correlation structures and are often a mixture of overlapping signal components that need to be separated. In order to facilitate multi-instrument analysis of correlated signals in general, we are developing the Universal Bayesian Imaging Kit (UBIK), a flexible and modular framework for high-fidelity Bayesian imaging. UBIK is designed to address these challenges using information field theory, which allows the consistent application of Bayesian logic to signal reconstruction, allowing uncertainties to be estimated. In particular, we use generative models to encode prior knowledge about the signals of interest in order to exploit spatial and spectral correlations and thereby improve their reconstruction from noisy data and enhance the component separation. Here, we show the application of UBIK to Poisson-noise-affected merged X-ray data of eROSITA, allowing data sets from different observations to be combined. This provides an enhanced and high quality visualisation of extended sources, point sources and background individually.
