• Corpus ID: 1152906

Jet Constituents for Deep Neural Network Based Top Quark Tagging

  title={Jet Constituents for Deep Neural Network Based Top Quark Tagging},
  author={Jannicke Pearkes and Wojciech Fedorko and Alison Lister and Colin Gay},
Recent literature on deep neural networks for tagging of highly energetic jets resulting from top quark decays has focused on image based techniques or multivariate approaches using high-level jet substructure variables. Here, a sequential approach to this task is taken by using an ordered sequence of jet constituents as training inputs. Unlike the majority of previous approaches, this strategy does not result in a loss of information during pixelisation or the calculation of high level… 

PyTorch Neural Networks and Track Analysis for Top Quark Tagging

Deep Neural Networks and Long Short-Term Memory networks were built in PyTorch to compare their performances to previously tested Keras models and it was found that keeping 100 pT ordered tracks with pT greater than 1 GeV could retain relevant information for jet classification while minimizing noise and computing time.

Long Short-Term Memory (LSTM) networks with jet constituents for boosted top tagging at the LHC

Several network architectures, methods of ordering of jet constituents, and input pre-processing are studied and the applicability of architectures incorporating Long Short-Term Memory (LSTM) networks is explored.

Interpretability Study on Deep Learning for Jet Physics at the Large Hadron Collider

This work takes jet tagging problem at the LHC as an example, using recursive neural networks as a starting point, and aims at a thorough understanding of the behaviour of the physics-oriented DNNs and the information encoded in the embedding space.

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.

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 modifications in heavy-ion collisions

Jet interactions in a hot QCD medium created in heavy-ion collisions are conventionally assessed by measuring the modification of the distributions of jet observables with respect to the

Deep-learning jets with uncertainties and more

The main features of Bayesian versions of established deep-learning taggers are illustrated and it is shown how they capture statistical uncertainties from finite training samples, systematics related to the jet energy scale, and stability issues through pile-up.

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.

Jet charge and machine learning

It is found that both neural networks that incorporate distance within the jet as an input and boosted decision trees including radial distance information can provide significant improvement in jet charge extraction over current methods.

Equivariant energy flow networks for jet tagging

A variant of the Energy Flow Network architecture is developed based on the Deep Sets formalism, incorporating permutation-equivariant layers, and it is found that equivariant Energy Flow Networks have similar performance to Particle Flow Networks, which are superior to standard EFNs.



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

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.

Deep learning in color: towards automated quark/gluon jet discrimination

A bstractArtificial intelligence offers the potential to automate challenging data-processing tasks in collider physics. To establish its prospects, we explore to what extent deep learning with

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.

Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks

Machine learning methods incorporating deep neural networks have been the subject of recent proposals for new hadronic resonance taggers. These methods require training on a data set produced by an

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 — 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.

Identification of Jets Containing b-Hadrons with Recurrent Neural Networks at the ATLAS Experiment

The expected performance of the RNN based b-tagging algorithm in simulated tt̄ events created in proton–proton collisions at √ s = 13 TeV is presented.

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

Identification of High-Momentum Top Quarks, Higgs Bosons, and W and Z Bosons Using Boosted Event Shapes

At the Large Hadron Collider, numerous physics processes expected within the standard model and theories beyond it give rise to very high momentum particles decaying to multihadronic final states.