Weniger's grant-winning proposal was titled 'DarkGenerators – Interpretable Large Scale Deep Generative Models for Dark Matter Searches'. Dark matter is five times more abundant in the universe than visible matter. Yet, its nature remains unknown and constitutes one of the most exciting and complex research questions today.
The project will use advanced data science methods to enhance and accelerate the interpretation of astrophysical and collider data in the search for signals of dark matter. Deep generative models and differentiable probabilistic programming will be used to construct a framework for the fast and precise inference of high-dimensional data models.
The eTEC-BIG call aims to support research and development of innovative eScience technologies and software associated with big data handling, big data analytics and related computational methods, driven by a direct demand from any research area that can be identified broadly by the term ‘Big Science’.
Each of the three winning projects will receive a grant consisting of funds and in-kind support by research engineers from the eScience Center and technology and e-Infrastructure experts from SURFsara.