240 citations · 508 across the 12 of their papers we have counts for
5 papers · 1 filter
A Non-Linear Kalman Filter for track parameters estimation in High Energy Physics
Xiaocong Ai, Heather M. Gray, Andreas Salzburger +1
The Kalman Filter is a widely used approach for the linear estimation of dynamical systems and is frequently employed within nuclear and particle physics experiments for the recons…
A Common Tracking Software Project
Xiaocong Ai, Corentin Allaire, Noemi Calace +29
The reconstruction of the trajectories of charged particles, or track reconstruction, is a key computational challenge for particle and nuclear physics experiments. While the tunin…
The Tracking Machine Learning challenge : Throughput phase
Sabrina Amrouche, Laurent Basara, Paolo Calafiura +18
This paper reports on the second "Throughput" phase of the Tracking Machine Learning (TrackML) challenge on the Codalab platform. As in the first "Accuracy" phase, the participants…
A GPU-based Kalman Filter for Track Fitting
Xiaocong Ai, Georgiana Mania, Heather M. Gray +2
Computing centres, including those used to process High-Energy Physics data and simulations, are increasingly providing significant fractions of their computing resources through h…
Porting HEP Parameterized Calorimeter Simulation Code to GPUs
Zhihua Dong, Heather Gray, Charles Leggett +3
The High Energy Physics (HEP) experiments, such as those at the Large Hadron Collider (LHC), traditionally consume large amounts of CPU cycles for detector simulations and data ana…