A MERA-based autoencoder supplies a locality-aware hierarchical inductive bias that improves reconstruction-based anomaly detection for collider jets, with disentanglers providing benefit at strong compression bottlenecks.
Butter et al.,The Machine Learning landscape of top taggers,SciPost Phys.7 (2019) 014, [arXiv:1902.09914]
7 Pith papers cite this work. Polarity classification is still indexing.
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OmniMol transfers a billion-jet pre-trained PET foundation model from HEP to molecular dynamics via an interaction-matrix attention bias, delivering strong performance on the oMol dataset with minimal fine-tuning and fast inference.
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.
Spectral-function features fed to a dense network improve expected gluino-mass reach by ~150 GeV over ATLAS Gtt-0L-C and ~250 GeV over kinematics-only ML for the fully hadronic channel.
Equivariant jet taggers suppress frame-dependent pseudorapidity while encoding jet mass and N-subjettiness strongly, with bivector channels negligible and vector channels dominant for top tagging.
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.
citing papers explorer
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Quantum-Inspired Tensor Network Autoencoders for Anomaly Detection: A MERA-Based Approach
A MERA-based autoencoder supplies a locality-aware hierarchical inductive bias that improves reconstruction-based anomaly detection for collider jets, with disentanglers providing benefit at strong compression bottlenecks.
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OmniMol: Transferring Particle Physics Knowledge to Molecular Dynamics with Point-Edge Transformers
OmniMol transfers a billion-jet pre-trained PET foundation model from HEP to molecular dynamics via an interaction-matrix attention bias, delivering strong performance on the oMol dataset with minimal fine-tuning and fast inference.
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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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Machine learning fully hadronic events with spectral functions
Spectral-function features fed to a dense network improve expected gluino-mass reach by ~150 GeV over ATLAS Gtt-0L-C and ~250 GeV over kinematics-only ML for the fully hadronic channel.
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What Do Lorentz-Equivariant Jet Taggers Learn?
Equivariant jet taggers suppress frame-dependent pseudorapidity while encoding jet mass and N-subjettiness strongly, with bivector channels negligible and vector channels dominant for top tagging.
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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.