Application of Machine Learning Algorithms in the Identification of Heavy Quark Jets Produced in Simulated Hadronic Collisions.

• Grant number: 24/08408-3
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: September 01, 2024
• End date: August 31, 2025
• Field of knowledge: Physical Sciences and Mathematics - Physics - Nuclear Physics
• Principal Investigator: Mauro Rogerio Cosentino
• Grantee: Vinicius Elias da Silva
• Host Institution: Centro de Ciências Naturais e Humanas (CCNH). Universidade Federal do ABC (UFABC). Ministério da Educação (Brasil). Santo André , SP, Brazil

Abstract

This project aims to extend a previous study of event simulation in hadronic collisions, focusing on the application of machine learning algorithms to analyze the obtained data. Based on the initial results of jet production simulations, Logistic Regression and Boosted Decision Trees (BDT) will be used for the identification and classification of jets containing hadrons with charm and bottom quarks. The main tools to be used include, in addition to those already employed in the previous study, the TMVA analysis package from ROOT, with the objective of developing efficient methods for heavy-flavor jets tagging.