Track Seed Classification with Deep Neural Networks

TL;DR

This paper explores using deep neural networks to improve seed classification in particle track reconstruction at the LHC, demonstrating high accuracy and speed, and proposing potential replacement of traditional seed generation methods.

Contribution

It introduces a neural network-based approach for seed classification that outperforms heuristics and can potentially replace existing seed generation algorithms.

Findings

Neural networks can classify seeds with high accuracy.

Deep learning offers significant speed advantages.

Potential to replace traditional seed generation methods.

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.