Speaker
Description
Long-lived particles (LLPs) are present in many Standard Model extensions and could provide solutions to long-standing problems in modern physics. In this work, machine-learning based techniques are developed to probe for the presence of such particles, specifically Heavy Neutral Leptons (HNLs) and Axion-Like Particles (ALPs), decaying in the LHCb muon detector. Their decays will produce electromagnetic or hadronic showers, which can be reconstructed by effectively turning the muon system into a sampling calorimeter.
The algorithms are designed for offline analysis and make use of offline saved events in which all raw detector hits are stored. To overcome discrepancies between simulation and reality, a hybrid data strategy is employed, combining real-data and simulation datasets for training purposes. The approach integrates these machine-learning methods with standard techniques, such as hit clustering, for efficient offline processing of large datasets. This enables the use of raw information from the detector while maintaining computational efficiency.