Application of Machine Learning Algorithms to Simulation Data in High Energy Physics

• Grant number: 24/08338-5
• 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: Gabriel Picholari da Cunha
• 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 continue a previous one and focuses on applying machine learning algorithms to data obtained from event simulations in high energy physics experiments. Using simulations performed with the Pythia 8 program, the goal is to apply Logistic Regression and Boosted Decision Trees (BDT) algorithms to enhance data analysis and interpretation. By the end of the project, it is expected that the student will have acquired the ability to integrate machine learning techniques into high energy physics analyses, contributing to a more robust analysis of the simulated data.