324 citations · 2.3k across the 39 of their papers we have counts for
41 papers
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…
Machine Learning scientific competitions and datasets
David Rousseau, Andrey Ustyuzhanin
A number of scientific competitions have been organised in the last few years with the objective of discovering innovative techniques to perform typical High Energy Physics tasks,…
The Tracking Machine Learning challenge : Accuracy phase
Sabrina Amrouche, Laurent Basara, Paolo Calafiura +24
This paper reports the results of an experiment in high energy physics: using the power of the "crowd" to solve difficult experimental problems linked to tracking accurately the tr…
Machine Learning in High Energy Physics Community White Paper
Kim Albertsson, Piero Altoe, Dustin Anderson +125
Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by a…
HEP Community White Paper on Software trigger and event reconstruction: Executive Summary
Johannes Albrecht, Kenneth Bloom, Tommaso Boccali +39
Realizing the physics programs of the planned and upgraded high-energy physics (HEP) experiments over the next 10 years will require the HEP community to address a number of challe…