Search for Heavy Higgs Bosons and Axion-Like Particles with the CMS Experiment via Deep Neural Networks

DASHH Doctoral Researcher: Jörn Bach

Supervisors: Prof. Christian Schwanenberger (DESY, UHH), Dr. Alexander Grohsjean (DESY): Prof. Peer Stelldinger (HAW)

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. In principle we search both for Dark Matter particles and for the particles that mediate their interaction with known particles. Those could be, for example, exotic heavy Higgs bosons or Axion-like particles (ALPs) with certain CP symmetry property. To classify the data into potential Dark Matter candidates and known elementary particles, a deep neural network will be implemented, trained, and fine-tuned. There is a special challenge here, since the new physics will interfere with the known processes in theory leading to a complicated expected signal+background topology that has to be separated from a background-only hypothesis. To improve the learning properties of deep neural networks in analyses with such large interferences, new methods will be explored to create new weight rebalancing methods taking into account the variance of the loss of the weight update itself rather than just the variance of the propagated net activation. New combinatorial approaches will be investigated to handle negative event weights due to interferences consistently which will be a huge step forward in high energy physics searches.