Collaborative Research: Particle Tracking at High Luminosity on Heterogeneous, Parallel Processor Architectures

Cornell University, Ithaca NY

Investigators

Abstract

Particle physics experiments at the Large Hadron Collider (LHC) at CERN seek to explore fundamental questions in modern physics, such as how particles attain mass, why gravity is weak, and the nature of dark matter. The large quantity of data produced at experimental facilities such as the LHC requires the development of complex pattern recognition algorithms and software techniques to achieve the scientific goals of these physics programs. This project will investigate new algorithms and techniques for data analysis using emerging computing processor architectures. These activities will enable the LHC experiments to take data more efficiently and improve the quality of the data that is recorded in order to extend the reach of the next generation of discoveries from planned hardware upgrades at the LHC over the next decade. The results of this research will significantly reduce the cost of computing for all LHC experiments. Software source code tools will be made available to the particle physics community. The investigators will host workshops to train post-doctoral fellows and graduate students from all areas of particle physics on how to use these advanced techniques. This training is valuable preparation for dealing with big data science in general. This project will support research focused on novel compute architectures for parallelized and vectorized charged particle track reconstruction. This research will improve the reach of energy-frontier particle physics experiments, such as the ATLAS, CMS, LHCb and ALICE experiments at the LHC and any other fields where studying the passage of charged particles is of critical importance. The science targeted in this project includes studying the properties of the Higgs boson, probing dark matter by searching for supersymmetry, and exploring the unknown by looking for such proposed effects as large extra space-time dimensions.