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The Tracking Machine Learning Challenge: Accuracy Phase
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 trajectory of particles in the Large Hadron Collider
(LHC). This experiment took the form of a machine learning challenge organized
in 2018: the Tracking Machine Learning Challenge (TrackML). Its results were
discussed at the competition session at the Neural Information Processing
Systems conference (NeurIPS 2018). Given 100.000 points, the participants had
to connect them into about 10.000 arcs of circles, following the trajectory of
particles issued from very high energy proton collisions. The competition was
difficult with a dozen front-runners well ahead of a pack. The single
competition score is shown to be accurate and effective in selecting the best
algorithms from the domain point of view. The competition has exposed a
diversity of approaches, with various roles for Machine Learning, a number of
which are discussed in the documen
The Tracking Machine Learning challenge : Accuracy phase
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 trajectory of particles in the Large Hadron Collider (LHC). This experiment took the form of a machine learning challenge organized in 2018: the Tracking Machine Learning Challenge (TrackML). Its results were discussed at the competition session at the Neural Information Processing Systems conference (NeurIPS 2018). Given 100.000 points, the participants had to connect them into about 10.000 arcs of circles, following the trajectory of particles issued from very high energy proton collisions. The competition was difficult with a dozen front-runners well ahead of a pack. The single competition score is shown to be accurate and effective in selecting the best algorithms from the domain point of view. The competition has exposed a diversity of approaches, with various roles for Machine Learning, a number of which are discussed in the documen