NNBAR is a proposed experiment for the European Spallation Source. Thegoal of the experiment is to observe the transformation n − ̄n. Currently a cutbased analysis is used to select signal events and discriminate against cosmic raybackground. To further increase the signal efficiency machine learning was used.Most machine learning algorithms resulted in a higher signal efficiency at the costof lowering the background rejection. However using the Linear DiscriminantAnalysis resulted in a new signal efficiency of 94% whilst having a predictedbackground rejection of roughly 100%. These results show that machine learningis a promising tool for increasing the signal efficiency at NNBAR.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:su-206690 |
| Date | January 2022 |
| Creators | Lejon, William |
| Publisher | Stockholms universitet, Fysikum |
| 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 |
Page generated in 0.0016 seconds