Algorithm for particle track reconstruction in SuperNEMO detector

AbstraktAbstract

The SuperNEMO experiment studies neutrinoless double beta decay, a rare nuclear process with profound implications for particle physics. Unlike most detectors focused only on energy measurement, SuperNEMO combines calorimetry with a driftcell tracking system, allowing full topological reconstruction of the events. This thesis presents a track reconstruction algorithm developed as a carefully structured combination of complementary techniques. We introduce an advanced recursive clustering method for tracker hits, based on a previously developed approach using the Legendre transform, and combine it with a highly optimized maximum likelihood procedure to ensure precision. We propose dedicated methods for resolving ambiguities in symmetric data configurations and for supporting polyline trajectory reconstruction to account for scattering. The algorithm has been implemented as a fully functional data processing module and achieves robust track reconstruction even in complex event topologies, contributing to the experiment’s goal of achieving ultra-low background conditions.

The SuperNEMO experiment studies neutrinoless double beta decay, a rare nuclear process with profound implications for particle physics. Unlike most detectors focused only on energy measurement, SuperNEMO combines calorimetry with a driftcell tracking system, allowing full topological reconstruction of the events. This thesis presents a track reconstruction algorithm developed as a carefully structured combination of complementary techniques. We introduce an advanced recursive clustering method for tracker hits, based on a previously developed approach using the Legendre transform, and combine it with a highly optimized maximum likelihood procedure to ensure precision. We propose dedicated methods for resolving ambiguities in symmetric data configurations and for supporting polyline trajectory reconstruction to account for scattering. The algorithm has been implemented as a fully functional data processing module and achieves robust track reconstruction even in complex event topologies, contributing to the experiment’s goal of achieving ultra-low background conditions.