Interpretable large scale deep generative models for Dark Matter searches
Astronomical observations have established that more than 80% of all matter in the Universe is made up of Dark Matter (DM). The determination of the nature of Dark Matter is one of the most important tasks of Physics and Astronomy; it will most likely be the result of a combination of all worldwide available experimental data.
Combining the worldwide data within the most general models of Dark Matter is the main objective of this project. This will test the models, determine the allowed parameter space for Dark Matter and help focus the effort for experimental searches. Finding viable solutions and exploring in a statistically convergent manner huge DM-model parameter spaces is the challenge, which this project team will approach with advanced eScience methods. Technical solutions to these questions have also multiple applications in society.
Promising ways to pinpoint Dark Matter
The project team will explore, compare and design algorithms to find (tiny, fragmented) solution areas in large multidimensional parameter spaces. Furthermore, we will explore methods to accelerate the computing machinery for DM searches. Finally we plan to investigate the possibility to make a (web-accessible) largely automated “DM model” database. With the help of such eScience machinery we will establish one of the most promising ways to pinpoint DM in the next years.