The High Energy Physics Tracking Machine Learning (TrackML)
challenge put into perspective
The LHC experiments need to reconstruct the trajectory of particles from the few precise measurements in the detector. One major process is to « connect the dots », that is associate together the points left by each particle. The complexity of the process is growing exponentially with the LHC luminosity, so that new algorithms are needed. The TrackML challenge (twitter: @trackmllhc ) is a two phases competition to tackle the issue: 100.000 points to be associated into 10.000 tracks in less than 100 seconds. The first phase (with no speed incentive) has run on Kaggle over the summer, while the second one (with a strong speed incentive) is just starting on Codalab. I will summarize the preliminary findings and perspective.
I will also put this in perspective with other efforts illustrating the growing impact of Machine Learning on High Energy Physics