Detecting neutrinos, especially ultra-high-energy (UHE) neutrinos, is inherently challenging. Highly sensitive detection devices are required to effectively capture these rare particles, which often results in significant noise in the data. This project focuses on enhancing the detection sensitivity of UHE neutrinos interacting with glacier ice by employing deep learning and transformer models. These models are trained on simulated data that mimics the radio signals produced by neutrino interactions in ice. The developed models have demonstrated improved performance compared to current hardware implementations, offering a promising advancement in neutrino detection technology.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-528582 |
| Date | January 2024 |
| Creators | Alin, Hans |
| 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 ; FYSPROJ1340 |
Page generated in 0.0017 seconds