Application of machine learning methods for strangeness reconstruction in heavy ion collision data from the ALICE experiment

• Grant number: 22/01562-1
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate (Direct)
• Start date: August 01, 2022
• End date: January 31, 2023
• Field of knowledge: Physical Sciences and Mathematics - Physics - Nuclear Physics
• Principal Investigator: Jun Takahashi
• Grantee: Gabriel Reis Garcia
• Supervisor: Alexander Kalweit
• Host Institution: Instituto de Física Gleb Wataghin (IFGW). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Institution abroad: European Organization for Nuclear Research (CERN), Switzerland
• Associated to the scholarship: 19/03743-0 - Application of multivariate analysis methods in heavy ion data from the ALICE experiment, BP.DD

Abstract

This is a proposal for a Research Internship Abroad project as part of PhD process "Application of multivariate analysis methods in Heavy Ion data from the ALICE experiment" that the focus is the development of Machine Learning (ML) techniques to analyze ALICE data. The project is being developed within the HadEx group of the IFGW at UNICAMP that is an official member of the ALICE collaboration. The goal of this project will be apply the ML techniques that were developed in the past three years of the PhD to reconstruct all the Multi-Strange Baryons in the complete data set measured by the ALICE experiment in 2015. Such analysis is a work in progress of the ALICE Strangeness Physics Analysis Group (PAG) and we know that ML can improve both efficiency and significance of this analysis as we showed in the last PhD Scientific Report in a subset of the experimental data. We believe that the direct contact and collaboration of the student and the other researchers from the Strangeness PAG at CERN is very important to both the development of the analysis and the conclusion of the main goal of the PhD. (AU)