Tagging with deep neural networks of top quarks decaying to hadrons

The Standard Model (SM) is a proposed well-established theory that describes successfully the physics of the elementary particles, and the fundamental interactions. However, it does not take into account gravitation, and it has some additional problems, as it does not solves the hierarchy problem, the mechanism of electroweak symmetry breaking. Besides that, SM does not contain an explanation for the energy-matter constitution of Universe, since dark matter and dark energy is not included in this model. In this context, the top quark plays a crucial role, as it is the most massive particle within the Standard Model, and it has a large coupling to Higgs Boson, the entity that explains the property of mass for elementary particles. Theories beyond SM, such as Supersymmetry, which attempts to solve the mentioned diculties, predict processes involving top pair decays as nal state products. Thus, identifying events including top quarks is important to the study and searches of new physics. Given the previous motivation, top quark tagging has recently acquired relevance. Thus, the current work proposes a machine learning approach for handling this task, and compares it with state-of-art top taggers. The project was depeloved based on simulations of top quark signal events and backgrounds with Pythia, Madgraph and Delphes

• Autores: Silvera Vega, Diego Felipe
• Tipo de recurso: Trabajo de grado de pregrado
• Fecha de publicación: 2018
• Institución: Universidad de los Andes
• Repositorio: Séneca: repositorio Uniandes
• Idioma: eng
• OAI Identifier: oai:repositorio.uniandes.edu.co:1992/40308
• Acceso en línea: http://hdl.handle.net/1992/40308
• Palabra clave: Aprendizaje automático (Inteligencia artificial); Teoría del campo cuántico; Partículas (Física nuclear); Física
• Rights: openAccess
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/