# Spectral analysis of jet substructure with neural networks: boosted Higgs case

@article{Lim2018SpectralAO,
title={Spectral analysis of jet substructure with neural networks: boosted Higgs case},
author={Sung Hak Lim and Mihoko M. Nojiri},
journal={Journal of High Energy Physics},
year={2018},
volume={2018},
pages={1-20}
}
• Published 9 July 2018
• Physics
• Journal of High Energy Physics
A bstractJets from boosted heavy particles have a typical angular scale which can be used to distinguish them from QCD jets. We introduce a machine learning strategy for jet substructure analysis using a spectral function on the angular scale. The angular spectrum allows us to scan energy deposits over the angle between a pair of particles in a highly visual way. We set up an artificial neural network (ANN) to find out characteristic shapes of the spectra of the jets from heavy particle decays…
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## References

SHOWING 1-10 OF 72 REFERENCES

• Physics
• 2011
We introduce a new jet shape — N-subjettiness — designed to identify boosted hadronically-decaying objects like electroweak bosons and top quarks. Combined with a jet invariant mass cut,
• Physics
• 2013
We develop a new method for tagging jets produced by hadronically decaying top quarks. The method is an application of shower deconstruction, a maximum information approach that was previously
• Physics
• 2014
A bstractOptimized jet substructure observables for identifying boosted topologies will play an essential role in maximizing the physics reach of the Large Hadron Collider. Ideally, the design of
• Computer Science
Journal of High Energy Physics
• 2019
This paper constructs a top-jet tagger based on a Convolutional Neural Network, and applies it to parton-level boosted top samples, with and without an additional gluon in the final state, showing that the jet observable defined by the CNN obeys the canonical definition of infrared safety.
• Physics
Physical review letters
• 2008
Top tagging can be used in tt[over ] events when one of the top quarks decays semileptonically, in events with missing energy, and in studies of b-tagging efficiency at high p{T}.
• Physics
• 2020
We introduce a novel jet substructure method which exploits the variation of observables with respect to a sampling of phase-space boundaries quantified by the variability. We apply this technique to
• Physics
Proceedings of International Conference on Hard and Electromagnetic Probes of High-Energy Nuclear Collisions — PoS(HardProbes2018)
• 2019
We study the phenomenon of jet quenching utilizing quark and gluon jet substructures as independent probes of heavy ion collisions. We exploit jet and subjet features to highlight differences between
• Taoli Cheng
• Computer Science
Computing and Software for Big Science
• 2018
The results show that RecNNs work better than the baseline boosted decision tree (BDT) by a few percent in gluon rejection rate, however, extra implementation of particle flow identification only increases the performance slightly.
Studying the substructure of jets has become a powerful tool for event discrimination and for studying QCD. Typically, jet substructure studies rely on Monte Carlo simulation for vetting their
• Physics
• 2017
A bstractMachine learning based on convolutional neural networks can be used to study jet images from the LHC. Top tagging in fat jets offers a well-defined framework to establish our DeepTop