Sectioning and Hyperparameter Optimization of the Topological Calorimeter Image Convolutional Anomaly Detection Algorithm (CICADA) in the CMS Level-1 Trigger

Abstract

This thesis reviews the Standard Model (SM) of particle physics and explores Beyond the Standard Model (BSM) theories, including supersymmetry, dark sectors, and neutral naturalness, with an emphasis on signatures searchable at particle collider experiments such as long-lived particles and Higgs processes. We examine detector hardware and data-taking software of the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC), focusing on the calorimeters and trigger system. In particular, we discuss the role of model-independent Real-time Anomaly Detection (RAD) in the CMS Level 1 Trigger (L1T) implemented in the Calorimeter Image Convolutional Anomaly Detection Algorithm (CICADA). We evaluate the impact of sectioning calorimeter inputs on CICADA performance and find no significant deviations, suggesting limited importance of spatial context. Furthermore, we carry out a Hyperparameter Optimization (HPO) search over Quantized Neural Networks (QNNs) for novel CICADA architectures, and identify several models with improved performance. This work contributes to the development of RAD for physics discovery at the LHC.