Tracking by Neural Nets

Current track reconstruction methods start with two points and then for each layer loop through all possible hits to find proper hits to add to that track. Another idea would be to use this large number of already reconstructed events and/or simulated data and train a machine on this data to find tracks given hit pixels. Training time could be long but real time tracking is really fast. Simulation might not be as realistic as real data but tracking efficiency is 100 percent for that while by using real data we would probably be limited to current efficiency. The fact that this approach can be a lot faster and even more efficient than current methods by using simulation data can make it a great alternative for current track reconstruction methods used in both triggering and tracking.