The paper introduces HyPER, a hypergraph-based graph neural network architecture designed to reconstruct short-lived particles in collider experiments. By leveraging hypergraph representation learning, HyPER builds more powerful and efficient representations of collider events, enabling accurate reconstruction of parent particles from final-state objects. In simulations, HyPER outperforms existing state‑of‑the‑art techniques while using fewer parameters, and its flexible hypergraph approach can be applied to a wide range of physics processes.
By Callum Birch-Sykes, Brian Le, Yvonne Peters, Ethan Simpson, Zihan Zhang
VyPER is a new geometric learning framework that reconstructs particle collider events by representing them as hypergraphs with a physics-inspired topology. It tackles two key tasks: assigning measured jets and leptons to their parent particles through supervised hyperedge classification, and predicting neutrino kinematics using a diffusion model, all optimized jointly with a shared loss function. The authors evaluate VyPER on various proton‑proton collision processes, showing improved performance over existing analytical and machine‑learning methods and enabling more precise measurements in Higgs, electroweak, and top‑quark studies.
By Lining Mao, Yvonne Peters, Ethan Simpson, Zihan Zhang
arXiv:2606. 14373v1 Announce Type: cross Abstract: The workflow from particle collision to physics analysis passes through a series of reconstruction steps that are traditionally modular and disconnected, with no shared representation linking low-level detector data to high-level analysis tasks.
By Farouk Mokhtar, Joosep Pata, Michael Kagan, Javier Duarte
The paper introduces a particle‑level generative model that uses residual‑quantized full‑event data to enable fast, ML‑based surrogate simulation for collider events. It demonstrates conditional generation from detector‑stable particles, explores scaling across dataset and model sizes, and shows that token‑level loss predicts downstream physical fidelity. The work offers an empirical framework for scalable collider full‑event generation using residual‑quantized representations.
By Dan Godi, Dmitrii Kobylianskii, Eilam Gross
arXiv:2604. 01313v2 Announce Type: replace Abstract: High-fidelity simulations and complex inverse problems, such as detector modeling and unfolding, are computationally intensive bottlenecks across subatomic physics, yet essential for accurate physical interpretation.
By Zeyu Xia, Tyler Kim, Trevor Reed, Judy Fox, Geoffrey Fox, Adam Szczepaniak
arXiv:2606. 14813v1 Announce Type: cross Abstract: Jet tagging at the Large Hadron Collider increasingly relies on deep learning models trained on massive simulated datasets, leading to high computational costs and limited robustness to detector mismodeling.
By Guillaume Letellier (LPCC), Antonin Vacheret (LPCC), Fr\'ed\'eric Jurie
The article reviews recent progress in applying machine learning to high‑energy nuclear physics, highlighting a shift from basic pattern recognition to physics‑integrated workflows. It covers calibrated Bayesian extraction of QCD matter properties, equation‑of‑state inference from heavy‑ion and neutron‑star data, generative event modeling, neural unfolding of weak signals, differentiable inverse solvers, gauge‑equivariant lattice‑field samplers, and holographic QCD reconstructions. The focus is on how ML models incorporate physical constraints—symmetries, conservation laws, causality, thermodynamic stability, and topology—and how uncertainty quantification and validation ensure reliable physics conclusions.
By Xun Chen, Weiyao Ke, Yu-Gang Ma, Long-Gang Pang, Kai Zhou
The paper introduces a data‑driven method for pairing events at the Large Hadron Collider using the energy mover's distance (EMD) to measure similarity, thereby creating augmentation‑free views for self‑supervised pre‑training. By matching distinct events based on EMD, the approach preserves the physics content of each event without handcrafted distortions. Experiments on QCD jets demonstrate that this pairing technique yields semantic jet embeddings with downstream discrimination power comparable to or better than traditional augmentation‑based baselines.
By Ho Fung Tsoi, Dylan Rankin
arXiv:2412. 10665v3 Announce Type: replace-cross Abstract: We introduce a foundation model for event classification in high-energy physics, built on a Graph Neural Network architecture and trained on 120 million simulated proton-proton collision events spanning 12 distinct physics processes.
By Joshua Ho, Benjamin Ryan Roberts, Shuo Han, Haichen Wang
arXiv:2607. 16144v1 Announce Type: cross Abstract: In this work we demonstrate that a single transformer-based generative model can capture Standard Model structure spanning five decades of invariant mass, from the sub-GeV regime to the TeV continuum, a range that no single Monte Carlo sample covers.
By Midori Kato, Kevin A. Urqu\'ia-Calder\'on, Inar Timiryasov, Oleg Ruchayskiy
arXiv:2606. 20437v1 Announce Type: cross Abstract: Charged-particle tracking -- reconstructing trajectories from sparse detector measurements -- is a fundamental high-energy-physics inference problem and a canonical example of learning under extreme combinatorial ambiguity.
By Siqi Miao, Shitij Govil, Jack P. Rodgers, Mia Liu, Javier Duarte, Shih-Chieh Hsu, Yuan-Tang Chou, Pan Li
The paper introduces a Deep Sets surrogate for optimal transport (OT) that respects key metric properties—non-negativity, exchange symmetry, and zero self-distance—while leaving the triangle inequality unconstrained. Applied to the Energy Mover's Distance between collider events, the Metric-Aware Particle Flow Network achieves percent‑level mean absolute percentage error and markedly higher inference throughput compared to other exact and approximate methods. The architectural constraints also dramatically reduce triangle‑inequality violations, improving geometric fidelity across a large set of held‑out event triplets.
By Lauren Hay, Rishabh Jain, Matt LeBlanc, Jennifer Roloff