Information field theory and its application to astronomical imaging

by Torsten Ensslin (MPI and LMU Munich (Germany))

Tuesday 16 Jan 2024, 13:00 → 14:00 Europe/Berlin

Seminar Room (Bldg. 28K) (FLASH)

Astronomical (and other) imaging is the process of translating measurement data into images of the sky (or other regions of interest) that can be understood by humans and analyzed physically. 

This requires that one or several fields like the brightness as a function of direction, time, and/or photon energy needs to be inferred. Inferring a physical field from data, however, is an ill posed problem, as the finite data can not alone constrain the infinite number of degrees of freedom of a function over continuous space. Domain knowledge has to regularize the set of possible solutions, however, usually significant uncertainties remain and need to be quantified. This can be done via information field theory (IFT), which is a mathematical formulation of probabilistic signal field inference that is related to modern AI/ML methodologies like generative models, however, without the need of training. 

Here, the basic concepts of IFT and its numerical implementation are introduced and some of its recent application to astrophysical datasets are presented that range from gamma ray astronomy over Galactic tomography to black hole filming.