Speaker
Description
Machine-learning techniques are becoming an increasingly important part of the design and physics reach of the proposed HIBEAM/NNBAR program at the European Spallation Source. Building on our previously published ML studies for particle identification and event reconstruction, we are developing a broader suite of ML tools to support detector optimization, vertex and event reconstruction, and signal–background discrimination for future neutron–antineutron searches and related rare-process measurements. As one component of this program we use graph-based deep learning to study vertex reconstruction in the TPC concept under study, using simulated datasets with controlled detector smearing to evaluate robustness across detector configurations. The work outlines current progress and illustrates how modern ML methods can be integrated into the HIBEAM/NNBAR analysis chain to improve reconstruction performance and inform detector design choices.