Track Seed Classification with Deep Neural Networks

Abstract

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. Thanks to immense parallelization benefits, it might even be worthwhile to completely replace the seed generation process with the Neural Network instead of just improving the seed quality of existing generators.