The instantaneous luminosity of the Large Hadron Collider is expected to increase in a few years time so that the amount of charged particle per proton bunch collision is expected to increase by a factor 10. In addition, the experiments plan a 10-fold increase of the readout rate. This will be a challenge for the ATLAS and CMS experiments, in particular for the tracking, which will be performed with a new all Silicon tracker in both experiments. Preliminary studies have shown that the CPU time to reconstruct an event increase many-fold, due to the combinatorial explosion at the pattern recognition stage, while the resource budget will be flat at best.
The TrackML challenge is being set up to engage Computer Scientists to tackle the problem with non HEP standard algorithms such as Convolutional Neural Network, Deep Neural Net or Monte Carlo Tree Search. A large data set of order one million events, ten billion tracks and one Terabyte will be created, so that there will be no lack of training data. The participants would compete to invent the fastest algorithm associating 3D points originating from the same charged particle, maintaining highest efficiency. They will do so by logging to a powerful platform, train on the data set, and submit their solution with an evaluation of its speed. The emphasis is to expose innovative approaches, rather than super-optimizing classical ones. If successful, it would improve the LHC physics reach.