The Large Hadron Collider (LHC) at CERN is the highest energy collider ever constructed. It consists of two counter-circulating proton beams made to interact in four locations around a 27 kilometer ring straddling the border between Switzerland and France. It is by some measures the largest man-made scientific device on the planet. The goal of the LHC is to probe the basic building blocks of matter and their interactions. For example, in 2012, the Higgs boson was discovered by the CMS and ATLAS collaborations.
The LHC collides proton beams at the center of our detectors. By measuring the energy and momentum of the escaping particles, we infer the existence of massive particles that were created in the collisions and measure the massive particles’ properties based on their decay products. The determination of the trajectories of charged particles ("tracks") from a set of positions of energy deposits from the various layers in our detector ("hits") plays a key role in identifying particles and measuring their charge and momentum. This pattern recognition problem—known as "track reconstruction" or simply "tracking"—is as a whole the most computationally complex and time-consuming step in the measurement process, as well as the most sensitive to increased activity in the detector, and traditionally, the least amenable to parallelized processing.
This project aims to develop fully vectorized and parallelized tracking algorithms based on the Kalman Filter for use in a collider experiment. The software will be usable with parallel architectures such as Intel Xeon processors and NVIDIA GPUs, yet maintain and extend the physics performance required for the challenges associated with the High Luminosity LHC (HL-LHC) planned for the 2020s.
The project also initiated training for young researchers through the first dedicated school on tools, techniques and methods for Computational and Data Science for High Energy Physics (CoDaS-HEP). The CoDaS-HEP school provides a broad introduction to these critical skills as well as an overview of applications in High Energy Physics. Specific topics covered at the school include: Parallel Programming, Big Data Tools and Techniques, and Machine Learning Technology and Methods, as well as a variety of practical skills. The inaugural CoDaS-HEP school took place on 10-13 July, 2017 at Princeton University. Subsequent schools took place on 23-27 July, 2018 and 22-26 July, 2019 at Princeton University.