Top-philic Machine Learning

Abstract

In this article, we review the application of modern machine-learning (ML) techniques to boost the search for processes involving the top quarks at the LHC. We revisit the formalism of Convolutional Neural Networks (CNNs), Graph Neural Networks (GNNs), and Attention Mechanisms. Based on recent studies, we explore their applications in designing improved top taggers, top reconstruction, and event classification tasks. We also examine the ML-based likelihood-free inference approach and generative unfolding models, focusing on their applications to scenarios involving top quarks.

arXiv commentA short review prepared by invitation for EPJ Special Topics issue. Version accepted for publication; 45 pages, 17 figures, 1 table; v2: typos corrected

References in corpus (137)

• A Comprehensive Survey on Graph Neural Networks
• Geometric deep learning: going beyond Euclidean data
• Dimension-Six Terms in the Standard Model Lagrangian
• Observation of Top Quark Production in Pbar-P Collisions
• The total top quark pair production cross-section at hadron colliders through O(alpha_S^4)
• Observation of the Top Quark
• Normalizing Flows: An Introduction and Review of Current Methods
• Higgs mass and vacuum stability in the Standard Model at NNLO
• Strong Dynamics and Electroweak Symmetry Breaking
• Identifying Boosted Objects with N-subjettiness
• Soft Drop
• The Standard Model as an Effective Field Theory
• SVD Approach to Data Unfolding
• Topological cell clustering in the ATLAS calorimeters and its performance in LHC Run 1
• ParticleNet: Jet Tagging via Particle Clouds
• CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks
• Fat Jets for a Light Higgs
• A Precision Measurement of the Mass of the Top Quark
• Deep learning in color: towards automated quark/gluon jet discrimination
• Jet-Images -- Deep Learning Edition
• Deep-learning Top Taggers or The End of QCD?
• Jet Substructure Classification in High-Energy Physics with Deep Neural Networks
• Energy Flow Networks: Deep Sets for Particle Jets
• The Machine Learning Landscape of Top Taggers
• Top, Higgs, Diboson and Electroweak Fit to the Standard Model Effective Field Theory
• Charge asymmetry in hadroproduction of heavy quarks
• A Monte Carlo global analysis of the Standard Model Effective Field Theory: the top quark sector
• The PDF4LHC21 combination of global PDF fits for the LHC Run III
• Deep-learned Top Tagging with a Lorentz Layer
• O new physics, where art thou? A global search in the top sector
• Top quark forward-backward asymmetry from new t-channel physics
• Seeing in Color: Jet Superstructure
• Jet-Images: Computer Vision Inspired Techniques for Jet Tagging
• How to GAN LHC Events
• OmniFold: A Method to Simultaneously Unfold All Observables
• Mining gold from implicit models to improve likelihood-free inference
• A generic anti-QCD jet tagger
• Threshold Resummation for the Top Quark Charge Asymmetry
• CP Violation in Top Physics
• Quark and Gluon Jet Substructure
• Right-handed charged currents in the era of the Large Hadron Collider
• A Guide to Constraining Effective Field Theories with Machine Learning
• Constraining top quark effective theory in the LHC Run II era
• Energy flow polynomials: A complete linear basis for jet substructure
• Naturally Speaking: The Naturalness Criterion and Physics at the LHC
• Disentangling Higgs-Top Couplings in Associated Production
• Pulling Out All the Tops with Computer Vision and Deep Learning
• JEDI-net: a jet identification algorithm based on interaction networks
• Probing Electroweak Top Quark Couplings at Hadron Colliders
• QCD-Aware Recursive Neural Networks for Jet Physics
• Weak interaction corrections to hadronic top quark pair production
• Machine Learning and LHC Event Generation
• Probing top flavour-changing neutral scalar couplings at the CERN LHC
• The forward-backward asymmetry of top quark production at the Tevatron in warped extra dimensional models
• Performance of top-quark and $W$-boson tagging with ATLAS in Run 2 of the LHC
• A likelihood-based reconstruction algorithm for top-quark pairs and the KLFitter framework
• Boosting the Direct CP Measurement of the Higgs-Top Coupling
• ABCNet: An attention-based method for particle tagging
• Invertible Networks or Partons to Detector and Back Again
• Anomaly detection with Convolutional Graph Neural Networks
• Lab-frame observables for probing the top-Higgs interaction
• Massive color-octet bosons and the charge asymmetries of top quarks at hadron colliders
• DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC
• How Much Information is in a Jet?
• How to GAN away Detector Effects
• Determing the CP Nature of a Neutral Higgs Boson at the LHC
• Mixed QCD and weak corrections to top quark pair production at hadron colliders
• Measurement of the cross section for top quark pair production in association with a W or Z boson in proton-proton collisions at $\sqrt{s}=$ 13 TeV
• Minkowski Functional Description of Microwave Background Gaussianity
• Understanding Event-Generation Networks via Uncertainties
• Top production at the Tevatron/LHC and nonstandard, strongly interacting spin one particles
• Measurement of the top quark mass in the all-jets final state at $\sqrt{s}=$ 13 TeV and combination with the lepton+jets channel
• Top polarization, forward-backward asymmetry and new physics
• Interaction networks for the identification of boosted $H\to b\overline{b}$ decays
• GANplifying Event Samples
• Generative Networks for Precision Enthusiasts
• Measurement of top quark pair production in association with a Z boson in proton-proton collisions at $\sqrt{s}=$ 13 TeV
• Probing stop pair production at the LHC with graph neural networks
• Top Quark Forward-Backward Asymmetry in the Large Invariant Mass Region
• Probing the Top-Higgs Yukawa CP Structure in dileptonic $t\bar{t}h$ with $M_2$-Assisted Reconstruction
• Unravelling $t\bar{t}h$ via the matrix element method
• Higher-order corrections for $tW$ production at high-energy hadron colliders
• When the Machine Chimes the Bell: Entanglement and Bell Inequalities with Boosted $t\bar{t}$
• Unveiling CP property of top-Higgs coupling with graph neural networks at the LHC
• Point Cloud Transformers applied to Collider Physics
• Casting a graph net to catch dark showers
• Single-top-quark production in the $t$-channel at NNLO
• Mapping Machine-Learned Physics into a Human-Readable Space
• Direct Measurement of the Top Quark Charge at Hadron Colliders
• Supervised Jet Clustering with Graph Neural Networks for Lorentz Boosted Bosons
• Top-quark mass measurement in the all-hadronic $t\bar{t}$ decay channel at $\sqrt{s}$ = 8 TeV with the ATLAS detector
• Revisiting Combinatorial Ambiguities at Hadron Colliders with MT2
• Lorentz Boost Networks: Autonomous Physics-Inspired Feature Engineering
• Equivariant Energy Flow Networks for Jet Tagging
• Symmetries, Safety, and Self-Supervision
• The Impact of Dimension-8 SMEFT Contributions: A Case Study
• Permutationless Many-Jet Event Reconstruction with Symmetry Preserving Attention Networks
• Machine learning the Higgs boson-top quark CP phase
• The Boosted Higgs Jet Reconstruction via Graph Neural Network
• Constraining the Strength and CP Structure of Dark Production at the LHC: the Associated Top-Pair Channel
• SMEFiT: a flexible toolbox for global interpretations of particle physics data with effective field theories
• Single top quark production as a probe of anomalous $tq\gamma$ and $tqZ$ couplings at the FCC-ee
• A New Method for Resolving Combinatorial Ambiguities at Hadron Colliders
• Infrared Safety of a Neural-Net Top Tagging Algorithm
• Reducing combinatorial uncertainties: A new technique based on MT2 variables
• Complete SMEFT predictions for four top quark production at hadron colliders
• Constraining CP-violation in the Higgs-top-quark interaction using machine-learning-based inference
• Neural Network-based Top Tagger with Two-Point Energy Correlations and Geometry of Soft Emissions
• Direct Higgs-top CP-phase measurement with $t\bar{t}h$ at the 14 TeV LHC and 100 TeV FCC
• Deep Unfolding with Normalizing Flow Priors for Inverse Problems
• Large Tree Level CP Violation in $e^{+}{e}^{-}\to t\bar{t}{H}^0$ in The Two Higgs Doublet Model
• Reports of My Demise Are Greatly Exaggerated: $N$-subjettiness Taggers Take On Jet Images
• Boosting to identify: pseudoscalar searches with di-leptonic tops
• Probing triple Higgs coupling with machine learning at the LHC
• The Matrix Element Method at next-to-leading order QCD for hadronic collisions: Single top-quark production at the LHC as an example application
• Jet-Parton Assignment in ttH Events using Deep Learning
• Particle Generative Adversarial Networks for full-event simulation at the LHC and their application to pileup description
• How to GAN Event Subtraction
• Learning to pinpoint effective operators at the LHC: a study of the $t\bar{t}b\bar{b}$ signature
• Topological Reconstruction of Particle Physics Processes using Graph Neural Networks
• Secondary Vertex Finding in Jets with Neural Networks
• Study of energy deposition patterns in hadron calorimeter for prompt and displaced jets using convolutional neural network
• Matrix Element Method at NLO for (anti-)${\mathbf{k}}_{\mathbf{t}}$-jet algorithms
• Top-quark physics at the Large Hadron Collider
• Resolving Combinatorial Ambiguities in Dilepton $t\bar{t}$ Event Topologies with Constrained $M_2$ Variables
• Improved Constraints on Effective Top Quark Interactions using Edge Convolution Networks
• Constraints on top quark flavor changing neutral currents using diphoton events at the LHC
• Resolving Extreme Jet Substructure
• New CP-odd Observable in H -> t \bar{t}
• From the Bottom to the Top -- Reconstruction of $t\bar{t}$ Events with Deep Learning
• On the BSM reach of four top production at the LHC
• Boosting New Physics Searches in $t\bar{t}Z$ and $tZj$ Production with Angular Moments
• The particle track reconstruction based on deep learning neural networks
• On two-body and three-body spin correlations in leptonic $t\bar{t}Z$ production and anomalous couplings at the LHC
• One-loop QCD and electroweak corrections to $t\bar{t}{Z}^0$ production at an $e^{+}{e}^{-}$ linear collider
• Resolving Combinatorial Ambiguities in Dilepton $t\bar{t}$ Event Topologies with Neural Networks
• Top-Quark Physics at the LHC

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