Abstract
Deep learning has led to several breakthroughs outside the field of high energy physics, yet in jet reconstruction for the CMS experiment at the CERN LHC it has not been used so far. This report shows results of applying deep learning strategies to jet reconstruction at the stage of identifying the original parton association of the jet (jet tagging), which is crucial for physics analyses at the LHC experiments. We introduce a custom deep neural network architecture for jet tagging. We compare the performance of this novel method with the other established approaches at CMS and show that the proposed strategy provides a significant improvement. The strategy provides the first multi-class classifier, instead of the few binary classifiers that previously were used, and thus yields more information and in a more convenient way. The performance results obtained with simulation imply a significant improvement for a large number of important physics analysis at the CMS experiment.
Highlights
The reconstruction of jets, sprays of particles that originate from the hadronization process of a quark or gluon, is a central element in high energy physics collider experiments
Deep learning has led to several breakthroughs outside the field of high energy physics, yet in jet reconstruction for the CMS experiment at the CERN LHC it has not been used so far
This report shows results of applying deep learning strategies to jet reconstruction at the stage of identifying the original parton association of the jet, which is crucial for physics analyses at the LHC experiments
Summary
The reconstruction of jets, sprays of particles that originate from the hadronization process of a quark or gluon, is a central element in high energy physics collider experiments. The use of recurrent neural networks was proposed [5, 6] in context of heavy flavor and other jet tagging. We compare the generic tagging capabilities to other deep neural network architectures for quark vs gluon classification.
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