Track Seed Classification with Deep Neural Networks

Understand

Future upgrades to the LHC will pose considerable challenges for traditional particle track reconstruction methods.

• We investigate how artificial Neural Networks and Deep Learning could be used to complement existing algorithms to increase performance.
• Generating seeds of detector hits is an important phase during the beginning of track reconstruction and improving the current heuristics of seed generation seems like a feasible task.
• We find that given sufficient training data, a comparatively compact, standard feed-forward neural network can be trained to classify seeds with great accuracy and at high speeds.