Awesome High Energy Physics — Phenomenology — curated papers, datasets & benchmarks

Explainable AI for Jet Tagging: A Comparative Study of GNNExplainer, GNNShap, and GradCAM for Jet Tagging in the Lund Jet Plane (2026)

Pahal D. Patel et al.

1.78

E-PCN: Jet Tagging with Explainable Particle Chebyshev Networks Using Kinematic Features (2025)

Md Raqibul Islam et al.

1.56

Stable and Interpretable Jet Physics with IRC-Safe Equivariant Feature Extraction (2025)

Partha Konar et al.

1.39

Mixture-of-Experts Graph Transformers for Interpretable Particle Collision Detection (2025)

Donatella Genovese et al.

0.94

The GCE in a New Light: Disentangling the $\gamma$-ray Sky with Bayesian Graph Convolutional Neural Networks (2020)

Florian List et al.

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Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics (2020)

Raghav Kansal and Javier Duarte and Breno Orzari and Thiago Tomei and Maurizio Pierini and Mary Touranakou and Jean-Roch Vlimant and Dimitrios Gunopulos

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Jet tagging in the Lund plane with graph networks (2020)

Fr\'ed\'eric A. Dreyer and Huilin Qu

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Semi-Equivariant GNN Architectures for Jet Tagging (2022)

Daniel Murnane et al.

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Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges (2022)

Savannah Thais et al.

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IRC-safe Graph Autoencoder for unsupervised anomaly detection (2022)

Oliver Atkinson et al.

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Uncovering dark matter density profiles in dwarf galaxies with graph neural networks (2022)

Tri Nguyen et al.

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Jet tagging algorithm of graph network with HaarPooling message passing (2022)

Fei Ma et al.

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Do graph neural networks learn traditional jet substructure? (2022)

Farouk Mokhtar et al.

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Topological Reconstruction of Particle Physics Processes using Graph Neural Networks (2023)

Lukas Ehrke et al.

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Graph Structure from Point Clouds: Geometric Attention is All You Need (2023)

Daniel Murnane

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PCN: A Deep Learning Approach to Jet Tagging Utilizing Novel Graph Construction Methods and Chebyshev Graph Convolutions (2023)

Yash Semlani et al.

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HyperTrack: Neural Combinatorics for High Energy Physics (2023)

Mikael Mieskolainen

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A Comparison Between Invariant and Equivariant Classical and Quantum Graph Neural Networks (2023)

Roy T. Forestano et al.

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A case study of sending graph neural networks back to the test bench for applications in high-energy particle physics (2024)

Emanuel Pfeffer and Michael Wa{\ss}mer and Yee-Ying Cung and Roger Wolf and Ulrich Husemann

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Jet Discrimination with Quantum Complete Graph Neural Network (2024)

Yi-An Chen et al.

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Integrating Physics Inspired Features with Graph Convolution (2024)

Rameswar Sahu

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EggNet: An Evolving Graph-based Graph Attention Network for Particle Track Reconstruction (2024)

Paolo Calafiura et al.

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Quantum Rationale-Aware Graph Contrastive Learning for Jet Discrimination (2024)

Md Abrar Jahin et al.

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Lie-Equivariant Quantum Graph Neural Networks (2024)

Jogi Suda Neto et al.

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Pretrained Event Classification Model for High Energy Physics Analysis (2024)

Joshua Ho et al.

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Awesome High Energy Physics — Phenomenology

Datasets & benchmarks

Key papers

High Energy Physics — Phenomenology

Awesome High Energy Physics — Phenomenology

Datasets & benchmarks

Key papers