dc.contributor.author | Alvestad, Daniel | |
dc.date.accessioned | 2018-09-10T16:16:00Z | |
dc.date.available | 2018-09-10T16:16:00Z | |
dc.date.issued | 2018-06-27 | |
dc.date.submitted | 2018-06-26T22:00:13Z | |
dc.identifier.uri | https://hdl.handle.net/1956/18468 | |
dc.description.abstract | In this thesis, the collider signatures of the scenario with a tau-sneutrino next-to-lightest supersymmetric particle (NLSP) at LHC are studied using machine learning. The parameter region of the non-universal Higgs masses model, where the tau-sneutrino is the NLSP, is studied to find a parameter point which satisfies constraints from recent experimental results. We look at the tri-lepton signature from two same sign hadronic taus and a muon. This signature have its main contribution from the slepton and sneutrino pair production channel. The aim is to enhance detectability of this signature by using a deep neural network trained on monte carlo simulated collider events. The best performing deep neural network is a multi class classifier, which is compared to other neural network architectures and a boosted decision tree. The required integrated luminosity for a 5σ significance discovery using √s=13 TeV is found to be L(5σ)= (3.4 ±0.7)⨉10³ 1/fb. We find that the multi class deep neural network performs better by a factor of 2.0 than the traditional optimized cuts. | en_US |
dc.language.iso | eng | eng |
dc.publisher | The University of Bergen | en_US |
dc.subject | next-to-lightest supersymmetric particle | eng |
dc.subject | tau-sneutrino | eng |
dc.subject | collider signatures | eng |
dc.subject | Luminositet | nob |
dc.subject | Maskinlæring | nob |
dc.subject | Supersymmetri | nob |
dc.title | Enhancing detectablility of tau-sneutrino signatures using machine learning | en_US |
dc.type | Master thesis | |
dc.date.updated | 2018-06-26T22:00:13Z | |
dc.rights.holder | Copyright the Author. All rights reserved | en_US |
dc.description.degree | Masteroppgave i fysikk | en_US |
dc.description.localcode | MAMN-PHYS | |
dc.description.localcode | PHYS399 | |
dc.subject.realfagstermer | https://data.ub.uio.no/realfagstermer/c007657 | |
dc.subject.realfagstermer | https://data.ub.uio.no/realfagstermer/c003939 | |
dc.subject.realfagstermer | https://data.ub.uio.no/realfagstermer/c008716 | |
dc.subject.nus | 752199 | eng |
fs.subjectcode | PHYS399 | |
fs.unitcode | 12-24-0 | |