Enabling Discoveries at the Large Hadron Collider through Advanced Computation

Mark Neubauer, University of Illinois at Urbana-Champaign

Usage Details

Mark Neubauer, David Lesny, Larry Nelson, Benjamin Hooberman, Matt Zhang, Dewen Zhong, Benjamin Galewsky, Matthew Feickert, James Bonham, Kenyi Hurtado Anampa, Cody Kankel, Ryan Lin, Alice Perng

The goal of particle physics is to explain the Universe at its most fundamental level. The CERN Large Hadron Collider (LHC) is the world's most powerful particle accelerator. It was designed to elucidate the fundamental buildings blocks of matter and the forces that govern their interactions by colliding protons at the highest available energies. The University of Illinois is a member and key contributor to the ATLAS experiment, one of two general purpose experiments at the LHC. The unprecedented sophistication of the ATLAS detector and reconstruction software, combined with the complexity of the proton-proton collisions' environment, place enormous demands on scientific computing resources. This proposal will make use of the Blue Waters supercomputer in two areas that are critically important for the discovery of new phenomena at the LHC. We propose to use Blue Waters to (1) process, simulate, and analyze high-energy proton-proton collision data produced by the ATLAS experiment and (2) utilize modern deep learning approaches and software to identify highly-boosted Higgs bosons at the LHC which would signal the existence of new physics at the energy frontier. The proposed activities make use of the unique capabilities of Blue Waters and advances in machine learning technologies to substantially improve the sensitivity of searches for new phenomena a world's largest collider.