Detector reconstruction of γ-rays
| dc.contributor.author | Halldestam, Peter | |
| dc.contributor.author | Hesse, Cody | |
| dc.contributor.author | Rinman, David | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för fysik | sv |
| dc.contributor.examiner | Falk, Lena | |
| dc.contributor.supervisor | Heinz, Andreas | |
| dc.contributor.supervisor | Johansson, Håkan T. | |
| dc.date.accessioned | 2020-08-14T05:26:34Z | |
| dc.date.available | 2020-08-14T05:26:34Z | |
| dc.date.issued | 2020 | sv |
| dc.date.submitted | 2020 | |
| dc.description.abstract | The 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.coursecode | TIFX04 | sv |
| dc.identifier.uri | https://hdl.handle.net/20.500.12380/301461 | |
| dc.language.iso | eng | sv |
| dc.setspec.uppsok | PhysicsChemistryMaths | |
| dc.subject | artificial neural networks | sv |
| dc.subject | convolutional neural networks | sv |
| dc.subject | graph neural networks | sv |
| dc.subject | gamma ray reconstruction | sv |
| dc.subject | addback | sv |
| dc.subject | Crystal Ball | sv |
| dc.subject | TensorFlow | sv |
| dc.subject | Keras | sv |
| dc.title | Detector reconstruction of γ-rays | sv |
| dc.type.degree | Examensarbete för kandidatexamen | sv |
| dc.type.uppsok | M2 |