A Particle Transformer jet tagger contains a sparse six-head circuit whose source-relay-readout structure recovers most performance and whose residual stream preferentially encodes 2-prong energy correlators.
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Particle Transformer for Jet Tagging
Tool reference. 75% of classified Pith citations use this work as a method, library, or software dependency, not as a substantive claim.
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PHAT-JeT combines geometric message-passing with hierarchical patch attention to reach state-of-the-art accuracy and background rejection among resource-constrained jet tagging models on four benchmarks.
LP2B encoding converts Lund plane jet representations into Bloch sphere qubit states, enabling a QTTN that matches classical LundNet performance on polarization tagging and W/top tagging with three orders of magnitude fewer parameters and improved low-data regime results.
IAFormer uses boost-invariant pairwise quantities and differential attention to create a sparse Transformer that achieves state-of-the-art classification on top-quark and quark-gluon jet datasets while using over an order of magnitude fewer parameters than prior Particle Transformer models.
Collider events are represented as multivectors in Cl(1,3) ⊗ V_flav whose grade projections recover standard observables, intended as input for equivariant foundation models.
Neural networks for HEP tasks can be fooled at significant rates by subtle perturbations inside uncertainty envelopes, revealing hidden systematics not captured by conventional methods.
PaRT achieves >50% tagging efficiency for boosted H->WW jets at 1% background efficiency, decorrelated from jet mass, with data-to-simulation scale factors of 0.9-1.0 on 138 fb^{-1} of 13 TeV collisions.
Higgsformer achieves AUC 0.855 on t tbar H vs t tbar classification from raw hits, matching a Delphes-based Particle Transformer at ~40% b-tagging efficiency.
A hyper-graph neural network improves discrimination of four-top production at 13 TeV, raising expected significance from 5.13 to 9.11 and enabling projected 95% CL limits on five dimension-six SMEFT Wilson coefficients at current and HL-LHC luminosities.
Explainability techniques applied to LundNet show that assigned node importance correlates with classical jet substructure observables such as N-subjettiness ratios and energy correlation functions, with shifts across transverse-momentum regimes.
Semi-leptonic h to VV* decays retain an effective two-qutrit description for quantum tomography and entanglement after including finite fermion masses and NLO corrections.
E-PCN reaches 94.67% macro-accuracy on 10-class jet tagging by weighting graphs with angular separation, transverse momentum, momentum fraction, and invariant mass, with Grad-CAM showing the first two account for 76% of decisions and yielding gains over baseline PCN.
SAL-T enhances the linformer with spatially aware kinematic partitioning and convolutions to match full-attention transformer performance on jet tagging while keeping linear complexity and lower latency.
Deep learning on all particles via holistic analysis and Advanced Color Singlet Identification improves Higgs signal extraction up to sixfold in high-energy collisions.
Transformers trained on cosmic ray simulations learn physically plausible features in positional encodings for symmetric air showers and in attention mechanisms for galaxy-origin particles.
Adversarial training enhances robustness of jet tagging classifiers while preserving performance, with loss surface geometry providing insights into correlations and vulnerability.
Monte Carlo projections set 90% CL upper limits of 3.7e-7, 8.9e-7, 1.8e-7 and 4.1e-7 on invisible branching fractions of omega, phi, eta and eta' at STCF.
CMS sets an observed upper limit of 4.4 on the HH signal strength μ_HH in the 4b final state at 13.6 TeV, improving prior LHC results by more than a factor of two in the resolved topology.
Machine learning models trained on known hadron data and an extended Gürsey-Radicati mass formula predict masses for triply heavy baryons and numerous pentaquark states, agreeing with available data and forecasting unobserved states.
A review of initiatives to make LHC Monte Carlo event generations available as open data to minimize redundant simulations and resource use.
ATLAS and CMS have performed analyses of rare top quark production modes that constrain top couplings and search for new physics.
citing papers explorer
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Dissecting Jet-Tagger Through Mechanistic Interpretability
A Particle Transformer jet tagger contains a sparse six-head circuit whose source-relay-readout structure recovers most performance and whose residual stream preferentially encodes 2-prong energy correlators.
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Patch Hierarchical Attention Transformer for Efficient Particle Jet Tagging
PHAT-JeT combines geometric message-passing with hierarchical patch attention to reach state-of-the-art accuracy and background rejection among resource-constrained jet tagging models on four benchmarks.
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Lund Plane to Bloch (LP2B) Encoding for Object and Polarization Tagging with Quantum Jet Substructure
LP2B encoding converts Lund plane jet representations into Bloch sphere qubit states, enabling a QTTN that matches classical LundNet performance on polarization tagging and W/top tagging with three orders of magnitude fewer parameters and improved low-data regime results.
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IAFormer: Interaction-Aware Transformer network for collider data analysis
IAFormer uses boost-invariant pairwise quantities and differential attention to create a sparse Transformer that achieves state-of-the-art classification on top-quark and quark-gluon jet datasets while using over an order of magnitude fewer parameters than prior Particle Transformer models.
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Geometric algebra as the input language of collider foundation models
Collider events are represented as multivectors in Cl(1,3) ⊗ V_flav whose grade projections recover standard observables, intended as input for equivariant foundation models.
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Uncovering Hidden Systematics in Neural Network Models for High Energy Physics
Neural networks for HEP tasks can be fooled at significant rates by subtle perturbations inside uncertainty envelopes, revealing hidden systematics not captured by conventional methods.
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Particle transformers for identifying Lorentz-boosted Higgs bosons decaying to a pair of W bosons
PaRT achieves >50% tagging efficiency for boosted H->WW jets at 1% background efficiency, decorrelated from jet mass, with data-to-simulation scale factors of 0.9-1.0 on 138 fb^{-1} of 13 TeV collisions.
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Hits to Higgs: Hit-Level Higgs Classification from Raw LHC Detector Data Using Higgsformer
Higgsformer achieves AUC 0.855 on t tbar H vs t tbar classification from raw hits, matching a Delphes-based Particle Transformer at ~40% b-tagging efficiency.
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Probing SMEFT Operators through $t\bar{t}t\bar{t}$ Production with Hyper-Graph Neural Networks at the LHC
A hyper-graph neural network improves discrimination of four-top production at 13 TeV, raising expected significance from 5.13 to 9.11 and enabling projected 95% CL limits on five dimension-six SMEFT Wilson coefficients at current and HL-LHC luminosities.
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Explainable AI for Jet Tagging: A Comparative Study of GNNExplainer, GNNShap, and GradCAM for Jet Tagging in the Lund Jet Plane
Explainability techniques applied to LundNet show that assigned node importance correlates with classical jet substructure observables such as N-subjettiness ratios and energy correlation functions, with shifts across transverse-momentum regimes.
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Quantum Tomography and Entanglement in Semi-Leptonic $h\to VV^*$ Decays at Higher Orders
Semi-leptonic h to VV* decays retain an effective two-qutrit description for quantum tomography and entanglement after including finite fermion masses and NLO corrections.
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E-PCN: Jet Tagging with Explainable Particle Chebyshev Networks Using Kinematic Features
E-PCN reaches 94.67% macro-accuracy on 10-class jet tagging by weighting graphs with angular separation, transverse momentum, momentum fraction, and invariant mass, with Grad-CAM showing the first two account for 76% of decisions and yielding gains over baseline PCN.
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Spatially Aware Linear Transformer (SAL-T) for Particle Jet Tagging
SAL-T enhances the linformer with spatially aware kinematic partitioning and convolutions to match full-attention transformer performance on jet tagging while keeping linear complexity and lower latency.
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Learning from all particles in high-energy collisions
Deep learning on all particles via holistic analysis and Advanced Color Singlet Identification improves Higgs signal extraction up to sixfold in high-energy collisions.
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What exactly did the Transformer learn from our physics data?
Transformers trained on cosmic ray simulations learn physically plausible features in positional encodings for symmetric air showers and in attention mechanisms for galaxy-origin particles.
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Improving robustness of jet tagging algorithms with adversarial training: exploring the loss surface
Adversarial training enhances robustness of jet tagging classifiers while preserving performance, with loss surface geometry providing insights into correlations and vulnerability.
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Search for invisible decays of light mesons via $J/\psi \to VP$ $(V=\omega/\phi,P=\eta/\eta')$ decays at STCF
Monte Carlo projections set 90% CL upper limits of 3.7e-7, 8.9e-7, 1.8e-7 and 4.1e-7 on invisible branching fractions of omega, phi, eta and eta' at STCF.
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Improved results on Higgs boson pair production in the 4b final state
CMS sets an observed upper limit of 4.4 on the HH signal strength μ_HH in the 4b final state at 13.6 TeV, improving prior LHC results by more than a factor of two in the resolved topology.
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Comprehensive Mass Predictions: From Triply Heavy Baryons to Pentaquarks
Machine learning models trained on known hadron data and an extended Gürsey-Radicati mass formula predict masses for triply heavy baryons and numerous pentaquark states, agreeing with available data and forecasting unobserved states.
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Open LHC Monte Carlo Event Generation
A review of initiatives to make LHC Monte Carlo event generations available as open data to minimize redundant simulations and resource use.
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Rare top quark production and top quark properties in ATLAS and CMS
ATLAS and CMS have performed analyses of rare top quark production modes that constrain top couplings and search for new physics.