Anja Butter

Welcome! I am a postdoc at the ITP in Heidelberg, working on particle physics. My research interests include Higgs physics, any signs of physics beyond the Standard Model, and the development of machine learning techniques to learn more about both. As the LHC is collecting large amounts of data in the search for new physics, machine learning has become an exciting technique to analyze and learn from these data. While it is crucial to push the limits of particle physics with new developments and techniques, the obtained results have to be understood in a global context. Therefore I am interested as well in global analyses of models like effective field theory and supersymmetry in which we can combine different measurements.

Generative networks

A very promising machine learning application is the use of generative neural networks for event simulation. We have shown that generative adversarial networks can learn the full underlying phase space distribution of events to generate independent new samples. The flexibility of neural networks allows us to construct tools for previously unattempted tasks. For instance the subtraction GAN shown here learns to populate the phase space between two distributions represented by samples.

Global analyses of dark matter

Global analyses of dark matter models allow us to connect measurements from collider, direct and indirect detection measurements. The right hand plot shows regions of the NMSSM (next to minimal supersymmetric extension of the Standard Model) parameter space that are in agreement with limits from direct detection and the measured relic density. Color coded is the resulting invisible branching ratio of the Higgs. We see that there are many regions that would lead to an enhanced branching ratio, making it a promising signature to look for new physics.