Jet charge and machine learning

@article{Fraser2018JetCA,
  title={Jet charge and machine learning},
  author={Katherine Fraser and Matthew D. Schwartz},
  journal={Journal of High Energy Physics},
  year={2018},
  volume={2018},
  pages={1-18}
}
A bstractModern machine learning techniques, such as convolutional, recurrent and recursive neural networks, have shown promise for jet substructure at the Large Hadron Collider. For example, they have demonstrated effectiveness at boosted top or W boson identification or for quark/gluon discrimination. We explore these methods for the purpose of classifying jets according to their electric charge. We find that both neural networks that incorporate distance within the jet as an input and… 
View on Springer
link.springer.com
64 Citations
Background Citations
12
Methods Citations
6
Results Citations
1

64 Citations

Identifying the Quantum Properties of Hadronic Resonances using Machine Learning

With the great promise of deep learning, discoveries of new particles at the Large Hadron Collider (LHC) may be imminent. Following the discovery of a new Beyond the Standard model particle in an…

QCD-aware recursive neural networks for jet physics

This work presents a novel class of recursive neural networks built instead upon an analogy between QCD and natural languages, and extends the analogy from individual jets to full events, and shows for the first time an event-level classifier operating on all the stable particles produced in an LHC event.

Infrared safety of a neural-net top tagging algorithm

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.

Boosted W and Z tagging with jet charge and deep learning

We demonstrate that the classification of boosted, hadronically decaying, weak gauge bosons can be significantly improved over traditional cut-based and boosted decision tree-based methods using deep…

Infrared safety of a neural-net top tagging algorithm

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.

Deep learning jet substructure from two-particle correlations

A novel, two-particle correlation neural network (2PCNN) architecture is constructed by combining neural network based filters on 2PCs and a deep neural network for capturing jet kinematic information.

An efficient Lorentz equivariant graph neural network for jet tagging

LorentzNet is introduced, a new symmetry-preserving deep learning model for jet tagging that achieves the best tagging performance and improves significantly over existing state-of-the-art algorithms.

Machine learning-based jet and event classification at the Electron-Ion Collider with applications to hadron structure and spin physics

We explore machine learning-based jet and event identification at the future Electron-Ion Collider (EIC). We study the effectiveness of machine learning-based classifiers at relatively low EIC…

Automating the construction of jet observables with machine learning

Machine-learning assisted jet substructure tagging techniques have the potential to significantly improve searches for new particles and Standard Model measurements in hadronic final states.…

Robust Jet Classifiers through Distance Correlation.

A new method based on a novel application of "distance correlation," a measure quantifying nonlinear correlations, that achieves equal performance to state-of-the-art adversarial decorrelation networks but is much simpler and more stable to train is presented.
...

References

SHOWING 1-10 OF 51 REFERENCES

Recursive Neural Networks in Quark/Gluon Tagging

  • 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.

Quark jet versus gluon jet: fully-connected neural networks with high-level features

Jet identification is one of the fields in high energy physics that machine learning has begun to make an impact. More often than not, convolutional neural networks are used to classify jet images…

Jet Substructure Classification in High-Energy Physics with Deep Neural Networks

These experiments demonstrate that without the aid of expert features, deep neural networks with a mixture of locally connected and fully connected nodes match or modestly outperform the current state-of-the-art approach for discriminating between jets from single hadronic particles and overlapping jets from pairs of collimatedHadronic particles, and that such performance gains persist in the presence of pileup interactions.

QCD-aware recursive neural networks for jet physics

This work presents a novel class of recursive neural networks built instead upon an analogy between QCD and natural languages, and extends the analogy from individual jets to full events, and shows for the first time an event-level classifier operating on all the stable particles produced in an LHC event.

Jet Constituents for Deep Neural Network Based Top Quark Tagging

The jet classification method achieves a background rejection of 45 at a 50% efficiency operating point for reconstruction level jets with transverse momentum range of 600 to 2500 GeV and is insensitive to multiple proton-proton interactions at the levels expected throughout Run 2 of the LHC.

Jet substructure at the Large Hadron Collider: A review of recent advances in theory and machine learning

Jet-images — deep learning edition

This interplay between physicallymotivated feature driven tools and supervised learning algorithms is general and can be used to significantly increase the sensitivity to discover new particles and new forces, and gain a deeper understanding of the physics within jets.

Playing tag with ANN: boosted top identification with pattern recognition

A bstractMany searches for physics beyond the Standard Model at the Large Hadron Collider (LHC) rely on top tagging algorithms, which discriminate between boosted hadronic top quarks and the much…

Jet flavor classification in high-energy physics with deep neural networks

This work finds that the highest-level lowest-dimensionality expert information sacrifices information needed for classification, that the performance of current state-of-the-art taggers can be matched or slightly exceeded by deep-network-based taggers using only track and vertex information, and that classification using only lowest-level highest- dimensionality tracking information remains a difficult task for deep networks.

Jet-images: computer vision inspired techniques for jet tagging

A novel approach to jet tagging and classification through the use of techniques inspired by computer vision is introduced, and the performance of this technique introduces additional discriminating power over other substructure approaches, and gives significant insight into the internal structure of jets.
...