Abstract
Modern physics is based on both theoretical analysis and experimental validation. Complex scenarios like subatomic dimensions, high energy, and lower absolute temperature are frontiers for many theoretical models. Simulation with stable numerical methods represents an excellent instrument for high accuracy analysis, experimental validation, and visualization. High performance computing support offers possibility to make simulations at large scale, in parallel, but the volume of data generated by these experiments creates a new challenge for Big Data Science. This paper presents existing computational methods for high energy physics (HEP) analyzed from two perspectives: numerical methods and high performance computing. The computational methods presented are Monte Carlo methods and simulations of HEP processes, Markovian Monte Carlo, unfolding methods in particle physics, kernel estimation in HEP, and Random Matrix Theory used in analysis of particles spectrum. All of these methods produce data-intensive applications, which introduce new challenges and requirements for ICT systems architecture, programming paradigms, and storage capabilities.
Highlights
High Energy Physics (HEP) experiments are probably the main consumers of High Performance Computing (HPC) in the area of e-Science, considering numerical methods in real experiments and assisted analysis using complex simulation
The numerical experiments using HPC for HEP represent a new challenge for Big Data Science
This paper analyzes two aspects: the computational methods used in HEP (Monte Carlo methods and simulations, Markovian Monte Carlo, unfolding methods in particle physics, kernel estimation, and Random Matrix Theory) and the challenges and requirements for ICT systems to deal with processing of Big Data generated by HEP experiments and simulations
Summary
High Energy Physics (HEP) experiments are probably the main consumers of High Performance Computing (HPC) in the area of e-Science, considering numerical methods in real experiments and assisted analysis using complex simulation. The numerical experiments using HPC for HEP represent a new challenge for Big Data Science. This paper analyzes two aspects: the computational methods used in HEP (Monte Carlo methods and simulations, Markovian Monte Carlo, unfolding methods in particle physics, kernel estimation, and Random Matrix Theory) and the challenges and requirements for ICT systems to deal with processing of Big Data generated by HEP experiments and simulations. Equations or integrals are based on classical quadratures and Monte Carlo (MC) techniques These allow generating events in terms of particle flavors and four-momenta, which is useful for experimental applications.
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