568 citations · 769 across the 16 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…
Machine learning for surface prediction in ACTS
Benjamin Huth, Andreas Salzburger, Tilo Wettig
We present an ongoing R&D activity for machine-learning-assisted navigation through detectors to be used for track reconstruction. We investigate different approaches of training n…
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…
Hashing and metric learning for charged particle tracking
Sabrina Amrouche, Moritz Kiehn, Tobias Golling +1
We propose a novel approach to charged particle tracking at high intensity particle colliders based on Approximate Nearest Neighbors search. With hundreds of thousands of measureme…