Detector reconstruction of γ-rays

dc.contributor.authorHalldestam, Peter
dc.contributor.authorHesse, Cody
dc.contributor.authorRinman, David
dc.contributor.departmentChalmers tekniska högskola / Institutionen för fysiksv
dc.contributor.examinerFalk, Lena
dc.contributor.supervisorHeinz, Andreas
dc.contributor.supervisorJohansson, Håkan T.
dc.date.accessioned2020-08-14T05:26:34Z
dc.date.available2020-08-14T05:26:34Z
dc.date.issued2020sv
dc.date.submitted2020
dc.description.abstractThe study of nuclear reactions through measuring emitted ᵧ-rays becomes convoluted due to complex interactions with the detector crystals, leading to cross-talk between neighbouring elements. To distinguish ᵧ-rays of higher multiplicity, the detector data needs to be reconstructed. As a continuation on the works of earlier students, this thesis investigates the application of neural networks as a reconstruction method and compares it to the conventional addback algorithm. Three types of neural networks are proposed; one Fully Connected Network, a Convolutional Neural Network (CNN) and a Graph Neural Network (GNN). Each model is optimized in terms of structure and hyperparameters, and trained on simulated data containing isotropic ᵧ-rays, before finally being evaluated on realistic detector data. Compared to previous projects, all presented networks showed a more consistent reconstruction across the studied energy range, which is credited to the newly introduced momentum-based loss function. Among the three networks, the fully connected performed the best in terms of smallest average absolute difference between the correct and reconstructed energies, while having the fewest number of trainable parameters. By the same metric, none of the presented networks performed better than addback. They did, however, show a higher signal-tobackground ratio in the energy range of 3–6 MeV. Suggestions for further studies are also given.sv
dc.identifier.coursecodeTIFX04sv
dc.identifier.urihttps://hdl.handle.net/20.500.12380/301461
dc.language.isoengsv
dc.setspec.uppsokPhysicsChemistryMaths
dc.subjectartificial neural networkssv
dc.subjectconvolutional neural networkssv
dc.subjectgraph neural networkssv
dc.subjectgamma ray reconstructionsv
dc.subjectaddbacksv
dc.subjectCrystal Ballsv
dc.subjectTensorFlowsv
dc.subjectKerassv
dc.titleDetector reconstruction of γ-rayssv
dc.type.degreeExamensarbete för kandidatexamensv
dc.type.uppsokM2
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