Neural Network-based study on background for the Dark Leptonic Scalar model at NA64
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Physics (MPPHS), MSc
Publicerad
2024
Författare
Zaya, Emil
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
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.
Beskrivning
Ämne/nyckelord
CERN , NA64 , particle physics , simulation , GEANT4 , Neural Network , Python , C++ , beyond the standard model , dark matter