Banca de QUALIFICAÇÃO: VITOR PAULO CAMPISTA NUNES

Uma banca de QUALIFICAÇÃO de MESTRADO foi cadastrada pelo programa.
STUDENT : VITOR PAULO CAMPISTA NUNES
DATE: 23/05/2022
TIME: 15:00
LOCAL: https://meet.google.com/hqe-vqnv-yjp
TITLE:

Machine Learning Application to  Potential Discovery of  Dark Matter Vector isotriplet in Muon Colliders.

KEY WORDS:

Muon Colliders, Machine Learning, Dark Matter, Standard Model

PAGES: 51
BIG AREA: Ciências Exatas e da Terra
AREA: Física
SUBÁREA: Física das Partículas Elementares e Campos
SUMMARY:

The Standard Model (SM) of elementary particles successfully describes all known particles and their interactions, apart from gravitational interaction. Despite its recognized success, the SM still has some gaps in the description of the microscopic world and cosmological properties of the universe, such as the nature of Dark Matter (DM). Therefore, it is a great challenge of Particle Physics to search for a candidate particle for DM. A widely accepted hypothesis is that the DM is made up of weakly interacting massive particles (WIMP's).  Many models have been proposed considering the inclusion of WIMP candidates, in order to extend the SM symmetry group. In this dissertation, we intend to study a simple extension of the SM that adds as the only new component, a massive spin 1 field, known as the Dark Matter Vector Isotriplet Model (DMVIM). This model has two free parameters: the mass of the vector field and a Higgs coupling.  Therefore, it is intended to address the potential of discovery of DM particles through the implementation of DMVIM in future Muon colliders.  For the separation of events of interest and background events, a new strategy based on Machine Learning (ML) techniques will be used.

BANKING MEMBERS:
Externo ao Programa - 1552299 - EDSON WANDER DIAS
Interno - 1672479 - LIZARDO HENRIQUE CERQUEIRA MOREIRA NUNES
Presidente - 364974 - MARIA ALINE BARROS DO VALE