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

  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},
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… 

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.

Jet tagging in the Lund plane with graph networks

  • F. DreyerH. Qu
  • Computer Science, Physics
    Journal of High Energy Physics
  • 2021
This article introduces LundNet, a novel jet tagging method which relies on graph neural networks and an efficient description of the radiation patterns within a jet to optimally disentangle signatures of boosted objects from background events, showing significantly improved performance for top tagging compared to existing state-of-the-art algorithms.

Neural network-based top tagger with two-point energy correlations and geometry of soft emissions

A neural network is designed that considers two types of sub-structural features: two-point energy correlations, and the IRC unsafe counting variables of a morphological analysis of jet images, and shows a comparable classification performance to the convolutional neural network.

Searching for new physics with deep autoencoders

A potentially powerful new method of searching for new physics at the LHC, using autoencoders and unsupervised deep learning, which opens up the exciting possibility of training directly on actual data to discover new physics with no prior expectations or theory prejudice.

Morphology for jet classification

We introduce a morphological analysis with the Minkowski functionals (MFs) for tagging boosted jets. The MFs can be written in terms of convolutions, and we explain a potential relationship between

Leveraging universality of jet taggers through transfer learning

This article considers the graph neural networks LundNet and ParticleNet, and introduces two prescriptions to transfer an existing tagger into a new signal based either on fine-tuning all the weights of a model or alternatively on freezing a fraction of them.

Interpretable deep learning for two-prong jet classification with jet spectra

An interpretable network trained on the jet spectrum S2(R) which is a two-point correlation function of the jet constituents which is comparable to that of a convolutional neural network (CNN) trained on jet images, although the number of inputs and complexity of the architecture is significantly simpler than the CNN classifier.

Quark-gluon tagging: Machine learning vs detector

The performance of different methods, including a new LoLa tagger, without and after considering detector effects are shown, and two benchmark applications, mono-jet searches with a gluon-rich signal and di-jet resonances with a quark- rich signal are discussed.

A $W^\pm$ polarization analyzer from Deep Neural Networks

In this paper we train a Convolutional Neural Network to classify longitudinally and transversely polarized hadronic W± using the images of boosted W± jets as input. The images capture angular and

Combine and conquer: event reconstruction with Bayesian Ensemble Neural Networks

It is shown that an Ensemble Neural Network can reduce epistemic uncertainties and the entropy of the hypothesis by simultaneously exploiting various kinematic correlations of the system, which also makes the network less susceptible to a limitation in training sample size.



Identifying boosted objects with N-subjettiness

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,

Finding top quarks with shower deconstruction

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

Power counting to better jet observables

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

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.

Top tagging: a method for identifying boosted hadronically decaying top quarks.

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}.

Isolating color-singlet boson jets at the LHC using telescoping jet substructure

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

Probing heavy ion collisions using quark and gluon jet substructure

  • Y. ChienR. K. Elayavalli
  • 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

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.

QCD analysis of the scale-invariance of jets

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

Deep-learning top taggers or the end of QCD?

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