  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.

Instrumentation and Detectors;High Energy Physics - Experiment;Data Analysis, Statistics and Probability