Neural Network-based study on background for the Dark Leptonic Scalar model at NA64
| dc.contributor.author | Zaya, Emil | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för fysik | sv |
| dc.contributor.department | Chalmers University of Technology / Department of Physics | en |
| dc.contributor.examiner | Cederwall, Martin | |
| dc.contributor.supervisor | Crivelli, Paolo | |
| dc.date.accessioned | 2024-10-14T09:26:26Z | |
| dc.date.available | 2024-10-14T09:26:26Z | |
| dc.date.issued | 2024 | |
| dc.date.submitted | ||
| dc.description.abstract | The search for a particle candidate that could explain the origin of dark matter is a central goal in modern astro-particle physics. Numerous experiments employing various measurement strategies are being developed to try and understand this elusive phenomenon. The NA64 experiment situated at the north area of CERN, utilizing the CERN Super Proton Synchrotron (SPS), is an active target experiment aiming to look for signatures like missing energies with hopes of finding signals that correspond to Dark Matter (DM) particles. These dark particles are modelled to explain the physical process of kinetic mixing between the Standard Model (SM) and the hypothesised corresponding Dark Sector (DS). The main purpose of this project is to study the background for a Dark Leptonic Scalar model (DLS) using a highly accurate Monte Carlo simulation for the NA64 experiment. More precisely, the GEANT4 particle simulator was used for the NA64 experiment to simulate the results of the experimental setup used in 2023. The results of this was compared with real data taken in 2023, and a first step was benchmarking the simulation which was done by using dimuon (μμ) events. Furthermore, the simulation results were used as a means of perfecting the methods of event selection. The main source of background for DLS particle φ are μμ production, kaon κ and pion π decay. The main purpose of this thesis is to produce a trained Neural Network (NN) model that can be used for optimizing the selection of events. The background for the DLS φ was simulated and trained on a NN for selecting μμ events as a means of benchmarking the method. The selection of μμ using a trained NN is compared to traditional methods of selection, where an increase of 36 % of the final state events is seen with the NN selected data. A future study could be to simulate the DLS φ particles and train them on a NN to use for event selection. The hopes are to gain a higher signal-to-background ratio and a larger amount of data for the DLS model. | |
| dc.identifier.coursecode | TIFX05 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.12380/308916 | |
| dc.language.iso | eng | |
| dc.setspec.uppsok | PhysicsChemistryMaths | |
| dc.subject | CERN | |
| dc.subject | NA64 | |
| dc.subject | particle physics | |
| dc.subject | simulation | |
| dc.subject | GEANT4 | |
| dc.subject | Neural Network | |
| dc.subject | Python | |
| dc.subject | C++ | |
| dc.subject | beyond the standard model | |
| dc.subject | dark matter | |
| dc.title | Neural Network-based study on background for the Dark Leptonic Scalar model at NA64 | |
| dc.type.degree | Examensarbete för masterexamen | sv |
| dc.type.degree | Master's Thesis | en |
| dc.type.uppsok | H | |
| local.programme | Physics (MPPHS), MSc |