Speaker
Torsten Ensslin
(MPI for Astrophysics)
Description
Imaging, the process of converting data into images, is in general an ill-posed problem that requires regularization by additional information. Information field theory (IFT) provides a consistent framework to fuse measurement data and abstract knowledge on signal fields into an optimal image by using field theoretical methods in Bayesian inference. Here, the IFT based D$^3$PO algorithm will be introduced, and its application to Fermi and RXTE data presented. The future vision of an Unified Bayesian Imaging tooKIT (UBIK) for multi-dimensional and multi-instrumental imaging and the first steps towards it will be presented.
Primary author
Torsten Ensslin
(MPI for Astrophysics)
