Particle track reconstruction in dense environments such as the detectors of the High Luminosity Large Hadron Collider (HL-LHC) is a challenging pattern recognition problem. Silicon-based, high-granularity tracking sensors detect ionization charge deposited by particles as they propagate through the detector in a magnetic field. Pattern recognition tracking algorithms and subsequent estimation methods use this information to measure the curvature of particle trajectories and thus deduce the particles’ charge and momentum. Traditional algorithms are inherently sequential and scale poorly with the expected increases in detector occupancy in the HL-LHC conditions. Machine learning algorithms bring a lot of potential to this problem!
The challenge: A special « Seattle » simulation dataset similar to TrackML dataset is prepared, yielding 5000 points to be connected into 500 tracks. Seattle hackathon participants will be more than welcome to participate to TrackML challenge in kaggle platform as soon as it is online.
Two starting kit algorithms will be provided, one using sk-learn DBscan, one using Hough transform. Participants are invited to beat the baselines from the starting-kit, either by improvement of the starting kits algorithms or by brand new algorithms.