Anomaly Detection in Searches for Inelastic Dark Matter with a Dark Higgs

Anomaly Detection presents a complementary ansatz to traditional searches for Dark Matter. Instead of searching for specific Dark Matter models, anomalous data is identified and compared to different models. This thesis explores different autoencoder architectures as tools for Anomaly Detection at the Belle II experiments and presents the Inelastic Dark Matter model with a Dark Higgs as a potential use case.