Speaker
Description
The Large Hadron Collider (LHC) has driven major discoveries in particle physics, but further exploration of the energy frontier calls for the next generation of machines. A multi-TeV muon collider (MuC) has been proposed as a potential next step, but its implementation hinges on solving major reconstruction concerns. Muon decay produces an overwhelming flux of background noise in the form of beam-induced background (BIB); particle identification and tracking algorithms tuned for the relatively clean LHC environment fall short under BIB conditions and must be adapted accordingly. This work investigates particle reconstruction performance for the MAIA detector concept under MuC conditions using jet energy resolution and transverse momentum studies. A neutron gun validation study identified PFOs marked as false multi-TeV neutrons producing significant energy contamination in reconstructed clusters, pointing to major issues in the handling of neutral hadronic jets in the presence of BIB. These findings motivate a closer look at how neutral hadronic energy is clustered and separated from background, with possible improvements drawing on density-based clustering approaches such as CLUE3D, developed for CMS's High Granularity Calorimeter.