Evaluation of Neutron Tracker Algorithms for Land and Neuland

dc.contributor.authorGill, Johan
dc.contributor.authorWranne, Staffan
dc.contributor.departmentChalmers tekniska högskola / Institutionen för fundamental fysiksv
dc.contributor.departmentChalmers University of Technology / Department of Fundamental Physicsen
dc.date.accessioned2019-07-03T13:14:09Z
dc.date.available2019-07-03T13:14:09Z
dc.date.issued2011
dc.description.abstractThe focus of this report is the neutron detection for the ALADiN-LAND setup at GSI/FAIR. Especially the evaluation of algorithms for reconstructing neutrons inside the LAND-detector, based on simulated data using GEANT3/GEANT4. The algorithm used since 1990 has been compared with a new experimental probabilistic algorithm. The algorithms have also been tested with the next generation neutron detector concepts (NeuLAND). For LAND both algorithms perform similarly, with the main difference that the new algorithm is much slower. The performance of reconstructing the four vector for single incoming neutrons is reasonably good, but decrease with increased number of incoming neutrons. To increase the performance for larger number of incoming neutrons, modification of the existing algorithms are needed. Due to the fact that the existing algorithms are developed for LAND they do not work well for the next generation neutron detectors. The program structure in the original algorithm is too simple to describe the full complexity of neutron interactions in the detectors. The experimental algorithm on the other hand is too slow to be a realistic alternative for neutron reconstruction in NeuLAND. For these detectors the development of new algorithms is needed.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/181106
dc.language.isoeng
dc.setspec.uppsokPhysicsChemistryMaths
dc.subjectGrundläggande vetenskaper
dc.subjectTungjonsfysik
dc.subjectBasic Sciences
dc.subjectHeavy ion physics
dc.titleEvaluation of Neutron Tracker Algorithms for Land and Neuland
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeComplex adaptive systems (MPCAS), MSc

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