We study the possibility of applying deep learning algorithms, suchas Variational Autoencoders, on simulated particle collider data to detectBeyond the Standard Model events. In this report, we apply three dif-ferent processes of training the data for better eciency and the resultsof said training on detecting anomalies. Links to the training and testingdata can be found here: https://www.phenomldata.org/
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-435325 |
| Date | January 2021 |
| Creators | Alves Cardoso, Mário |
| Publisher | Uppsala universitet, Högenergifysik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | FYSAST ; FYSPROJ1210 |
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