Algorithm for particle track reconstruction in SuperNEMO detector
- NázevTitle
- Algorithm for particle track reconstruction in SuperNEMO detectorAlgorithm for particle track reconstruction in SuperNEMO detector
- Druh výsledkuResult type
- Kvalifikační práceThesis
- AutořiAuthors
- T. Křižák
- Vedoucí, konzultanti, oponentiSupervisors, advisors, reviewers
- L. Thorne, M. Macko
- Klíčová slovaKeywords
- maximum likelihood method, Legendre transform, neutrinoless double beta decay, neutrino physics, SuperNEMO, track reconstruction, polyline trajectories
- Časopis / citaceJournal / citation
- Praha: Defense date 2025-06-12. Master Thesis. CTU FNSPE. Department of Mathematics.
- RokYear
- 2025
- JazykLanguage
- eng
- ZáznamyRecords
- ProjektProject
- Detektory ionizujícího záření ve fundamentálních experimentech a aplikovaném výzkumuDetectors of ionizing radiation in fundamental experiments and applied research; Laboratoire Souterrain de Modane - účast ČRLaboratoire Souterrain de Modane – participation of the Czech Republic
- Citace ke staženíDownload citation
- TXT · BibTeX
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.