Speaker
Jerome Bobin
Description
Inverse problems are ubiquitous in astrophysics, ranging from image reconstruction to unmixing or unsupervised com-
ponent separation, but they often share common challenges: i) how to deal with ill-posedness, which mandates the design of effective and physically relevant regularisation, ii) how to deal with the deluge of data coming from current and future experiments and iii) quantifying uncertainties to allow a scientifically validated exploitation of these data. The combination of machine learning with statistical-grounded methods could be a way to tackle some of these challenges. To that end, we will show some recent advances in astrophysical imaging, with a particular focus on multispectral/hyperspectral X-ray and radio-imaging.