Search for Dark Matter with the CMS experiment via Neural Networks with Multi-Task Learning

Supervisors: Prof. Christian Schwanenberger (DESY, UHH), Dr. Christian Seifert (TUHH)

The big unknown in particle physics is Dark Matter. In order to detect the presence of Dark Matter, we use data coming from the CMS experiment at the Large Hadron Collider at CERN. To classify the data into potential Dark Matter candidates and known elementary particles, a deep neural network will be implemented, trained, and fine-tuned. To obtain further characteristic properties of Dark Matter particles, such as the nature of the interaction with known particles, an attribute learning model combined with multi-task learning techniques will be exploited. A stable classification with characteristic meta-information of Dark Matter will be a huge step forward for new physics searches since to our knowledge such an approach had not been applied ever in particle physics.