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Machine learning for simulation of HEP collisions with the CMS detector

Grant number: 19/16401-0
Support type:Scholarships abroad - Research Internship - Master's degree
Effective date (Start): October 01, 2019
Effective date (End): December 20, 2019
Field of knowledge:Physical Sciences and Mathematics - Physics
Principal Investigator:Thiago Rafael Fernandez Perez Tomei
Grantee:Breno Orzari
Supervisor abroad: Maurizio Pierini
Home Institution: Instituto de Física Teórica (IFT). Universidade Estadual Paulista (UNESP). Campus de São Paulo. São Paulo , SP, Brazil
Local de pesquisa : European Organization for Nuclear Research (CERN), Switzerland  
Associated to the scholarship:18/01398-1 - Search for new physics on the CMS experiment of the Large Hadron Collider, BP.MS

Abstract

The scientific endeavour of the Large Hadron Collider experiments depends critically on the swift analysis of the data produced by the collider. A vital component of that analysis is the large-scale production of simulated collisions to be compared with the observed data. The production campaigns usually comprise tens of billions of simulated collisions, with each collision taking more than one minute to be fully simulated. Recent developments in deep learning techniques - generative models and graph networks - may allow to speed up that process. This project proposes an in-depth study of the suitability of those techniques for the simulation process, using the CMS detector as a benchmark scenario.